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 83 (83.0%) zerosZeros
유입량(ms) has 31 (31.0%) zerosZeros
방류량(ms) has 31 (31.0%) zerosZeros
저수량(백만m3) has 31 (31.0%) zerosZeros
저수율 has 31 (31.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:55:42.683630
Analysis finished2023-12-10 10:55:52.219618
Duration9.54 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
31 
공주보
31 
구담보
31 
구미보
7 

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 31
31.0%
공주보 31
31.0%
구담보 31
31.0%
구미보 7
 
7.0%

Length

2023-12-10T19:55:52.347150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:52.562269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 31
31.0%
공주보 31
31.0%
구담보 31
31.0%
구미보 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190115
Minimum20190101
Maximum20190131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:52.782506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190102
Q120190107
median20190115
Q320190123
95-th percentile20190130
Maximum20190131
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5637709 × 10-7
Kurtosis-1.2602055
Mean20190115
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0190115 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T19:55:53.320953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190101 4
 
4.0%
20190103 4
 
4.0%
20190104 4
 
4.0%
20190105 4
 
4.0%
20190106 4
 
4.0%
20190107 4
 
4.0%
20190102 4
 
4.0%
20190126 3
 
3.0%
20190122 3
 
3.0%
20190123 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190101 4
4.0%
20190102 4
4.0%
20190103 4
4.0%
20190104 4
4.0%
20190105 4
4.0%
20190106 4
4.0%
20190107 4
4.0%
20190108 3
3.0%
20190109 3
3.0%
20190110 3
3.0%
ValueCountFrequency (%)
20190131 3
3.0%
20190130 3
3.0%
20190129 3
3.0%
20190128 3
3.0%
20190127 3
3.0%
20190126 3
3.0%
20190125 3
3.0%
20190124 3
3.0%
20190123 3
3.0%
20190122 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8002
Minimum4.28
Maximum62.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:53.544268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.28
5-th percentile4.3
Q14.32
median38.14
Q362.46
95-th percentile62.47
Maximum62.48
Range58.2
Interquartile range (IQR)58.14

Descriptive statistics

Standard deviation23.122526
Coefficient of variation (CV)0.66443658
Kurtosis-1.4051503
Mean34.8002
Median Absolute Deviation (MAD)24.33
Skewness-0.18447844
Sum3480.02
Variance534.6512
MonotonicityNot monotonic
2023-12-10T19:55:53.770474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
62.47 20
20.0%
4.31 14
14.0%
38.15 7
 
7.0%
38.14 7
 
7.0%
38.16 7
 
7.0%
62.46 6
 
6.0%
4.32 6
 
6.0%
4.3 6
 
6.0%
62.48 3
 
3.0%
38.12 3
 
3.0%
Other values (15) 21
21.0%
ValueCountFrequency (%)
4.28 1
 
1.0%
4.29 2
 
2.0%
4.3 6
6.0%
4.31 14
14.0%
4.32 6
6.0%
4.33 1
 
1.0%
4.34 1
 
1.0%
32.48 1
 
1.0%
32.51 3
 
3.0%
32.52 3
 
3.0%
ValueCountFrequency (%)
62.48 3
 
3.0%
62.47 20
20.0%
62.46 6
 
6.0%
62.45 1
 
1.0%
62.39 1
 
1.0%
38.22 1
 
1.0%
38.17 2
 
2.0%
38.16 7
 
7.0%
38.15 7
 
7.0%
38.14 7
 
7.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013141
Minimum0
Maximum0.2688
Zeros83
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:53.976901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.09487
Maximum0.2688
Range0.2688
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.047250282
Coefficient of variation (CV)3.5956382
Kurtosis19.867616
Mean0.013141
Median Absolute Deviation (MAD)0
Skewness4.4259478
Sum1.3141
Variance0.0022325891
MonotonicityNot monotonic
2023-12-10T19:55:54.180899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 83
83.0%
0.0235 3
 
3.0%
0.1228 1
 
1.0%
0.0934 1
 
1.0%
0.2659 1
 
1.0%
0.0326 1
 
1.0%
0.004 1
 
1.0%
0.2234 1
 
1.0%
0.1267 1
 
1.0%
0.0119 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
0.0 83
83.0%
0.0014 1
 
1.0%
0.004 1
 
1.0%
0.0104 1
 
1.0%
0.0119 1
 
1.0%
0.0168 1
 
1.0%
0.0235 3
 
3.0%
0.0238 1
 
1.0%
0.0326 1
 
1.0%
0.0417 1
 
1.0%
ValueCountFrequency (%)
0.2688 1
 
1.0%
0.2659 1
 
1.0%
0.2234 1
 
1.0%
0.1267 1
 
1.0%
0.1228 1
 
1.0%
0.0934 1
 
1.0%
0.0417 1
 
1.0%
0.0326 1
 
1.0%
0.0238 1
 
1.0%
0.0235 3
3.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.1328
Minimum0
Maximum119.079
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:54.380810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34.3955
Q3101.9655
95-th percentile110.25945
Maximum119.079
Range119.079
Interquartile range (IQR)101.9655

Descriptive statistics

Standard deviation43.179305
Coefficient of variation (CV)0.93597842
Kurtosis-1.3189714
Mean46.1328
Median Absolute Deviation (MAD)34.3955
Skewness0.49746189
Sum4613.28
Variance1864.4524
MonotonicityNot monotonic
2023-12-10T19:55:54.630679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
107.33 3
 
3.0%
111.37 2
 
2.0%
32.183 1
 
1.0%
34.018 1
 
1.0%
33.394 1
 
1.0%
33.885 1
 
1.0%
34.354 1
 
1.0%
34.437 1
 
1.0%
33.584 1
 
1.0%
Other values (57) 57
57.0%
ValueCountFrequency (%)
0.0 31
31.0%
32.183 1
 
1.0%
32.724 1
 
1.0%
32.907 1
 
1.0%
33.066 1
 
1.0%
33.266 1
 
1.0%
33.357 1
 
1.0%
33.394 1
 
1.0%
33.401 1
 
1.0%
33.584 1
 
1.0%
ValueCountFrequency (%)
119.079 1
1.0%
113.494 1
1.0%
112.207 1
1.0%
111.37 2
2.0%
110.201 1
1.0%
110.085 1
1.0%
109.84 1
1.0%
109.422 1
1.0%
109.35 1
1.0%
109.294 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.05712
Minimum0
Maximum118.732
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:54.865330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34.277
Q3100.57575
95-th percentile110.3934
Maximum118.732
Range118.732
Interquartile range (IQR)100.57575

Descriptive statistics

Standard deviation43.110652
Coefficient of variation (CV)0.9360258
Kurtosis-1.311969
Mean46.05712
Median Absolute Deviation (MAD)34.277
Skewness0.49965644
Sum4605.712
Variance1858.5284
MonotonicityNot monotonic
2023-12-10T19:55:55.105821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
107.33 3
 
3.0%
111.37 2
 
2.0%
32.262 1
 
1.0%
33.86 1
 
1.0%
33.473 1
 
1.0%
33.806 1
 
1.0%
34.275 1
 
1.0%
34.279 1
 
1.0%
33.426 1
 
1.0%
Other values (57) 57
57.0%
ValueCountFrequency (%)
0.0 31
31.0%
32.153 1
 
1.0%
32.262 1
 
1.0%
32.645 1
 
1.0%
33.066 1
 
1.0%
33.345 1
 
1.0%
33.426 1
 
1.0%
33.473 1
 
1.0%
33.53 1
 
1.0%
33.559 1
 
1.0%
ValueCountFrequency (%)
118.732 1
1.0%
116.46 1
1.0%
111.683 1
1.0%
111.37 2
2.0%
110.342 1
1.0%
110.201 1
1.0%
109.35 1
1.0%
109.316 1
1.0%
108.898 1
1.0%
108.583 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.35631
Minimum0
Maximum52.863
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:55.348564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.436
Q39.362
95-th percentile52.798
Maximum52.863
Range52.863
Interquartile range (IQR)9.362

Descriptive statistics

Standard deviation13.106637
Coefficient of variation (CV)1.7816863
Kurtosis7.797873
Mean7.35631
Median Absolute Deviation (MAD)2.436
Skewness2.9338585
Sum735.631
Variance171.78394
MonotonicityNot monotonic
2023-12-10T19:55:55.541133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 31
31.0%
2.436 14
14.0%
9.407 7
 
7.0%
9.362 7
 
7.0%
9.452 7
 
7.0%
2.443 6
 
6.0%
2.429 6
 
6.0%
9.271 3
 
3.0%
52.863 3
 
3.0%
52.798 3
 
3.0%
Other values (11) 13
13.0%
ValueCountFrequency (%)
0.0 31
31.0%
2.416 1
 
1.0%
2.422 2
 
2.0%
2.429 6
 
6.0%
2.436 14
14.0%
2.443 6
 
6.0%
2.45 1
 
1.0%
2.457 1
 
1.0%
9.045 1
 
1.0%
9.09 1
 
1.0%
ValueCountFrequency (%)
52.863 3
3.0%
52.798 3
3.0%
52.571 1
 
1.0%
9.724 1
 
1.0%
9.497 2
 
2.0%
9.452 7
7.0%
9.407 7
7.0%
9.362 7
7.0%
9.271 3
3.0%
9.226 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.164
Minimum0
Maximum111.4
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:55.741996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.7
Q3106.2
95-th percentile108.3
Maximum111.4
Range111.4
Interquartile range (IQR)106.2

Descriptive statistics

Standard deviation48.357297
Coefficient of variation (CV)1.0707045
Kurtosis-1.7523829
Mean45.164
Median Absolute Deviation (MAD)15.7
Skewness0.4556522
Sum4516.4
Variance2338.4282
MonotonicityNot monotonic
2023-12-10T19:55:55.921689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.0 31
31.0%
15.7 20
20.0%
15.6 8
 
8.0%
107.8 7
 
7.0%
107.3 7
 
7.0%
108.3 7
 
7.0%
106.2 3
 
3.0%
100.3 3
 
3.0%
100.1 3
 
3.0%
108.8 2
 
2.0%
Other values (8) 9
 
9.0%
ValueCountFrequency (%)
0.0 31
31.0%
15.5 1
 
1.0%
15.6 8
 
8.0%
15.7 20
20.0%
15.8 2
 
2.0%
99.7 1
 
1.0%
100.1 3
 
3.0%
100.3 3
 
3.0%
103.6 1
 
1.0%
104.2 1
 
1.0%
ValueCountFrequency (%)
111.4 1
 
1.0%
108.8 2
 
2.0%
108.3 7
7.0%
107.8 7
7.0%
107.3 7
7.0%
106.2 3
3.0%
105.7 1
 
1.0%
104.7 1
 
1.0%
104.2 1
 
1.0%
103.6 1
 
1.0%

Interactions

2023-12-10T19:55:50.600842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:43.210929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:44.368268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:45.599982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:46.930000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.272616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.386261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.769980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:43.388339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:44.508123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:45.848065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.136440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.431575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.541090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.930180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:43.560956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:44.660774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:46.034017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.318583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.591096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.748811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.082323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:43.737045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:44.800454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:46.177884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.543779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.749447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.953998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.305645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:43.905071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:44.949174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:46.418385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.709078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.909736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.122008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.476750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:44.072407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:45.086657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:46.577955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:47.891435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.086680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.302213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:51.654731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:44.230781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:45.464270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:46.718052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:48.081039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:49.237156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:50.456303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:55:56.058116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9190.8961.0000.996
일자/시간(t)0.0001.0000.0000.4050.0000.0000.1390.000
저수위(m)1.0000.0001.0000.0000.9190.8961.0000.996
강우량(mm)0.0000.4050.0001.0000.0000.0000.0000.000
유입량(ms)0.9190.0000.9190.0001.0000.9990.8650.888
방류량(ms)0.8960.0000.8960.0000.9991.0000.8270.882
저수량(백만m3)1.0000.1391.0000.0000.8650.8271.0000.805
저수율0.9960.0000.9960.0000.8880.8820.8051.000
2023-12-10T19:55:56.226309image/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)1.0000.0220.031-0.112-0.109-0.181-0.0680.000
저수위(m)0.0221.000-0.285-0.362-0.369-0.376-0.3311.000
강우량(mm)0.031-0.2851.0000.1930.2010.2480.1960.000
유입량(ms)-0.112-0.3620.1931.0000.9980.8920.9590.878
방류량(ms)-0.109-0.3690.2010.9981.0000.8830.9520.838
저수량(백만m3)-0.181-0.3760.2480.8920.8831.0000.9460.995
저수율-0.068-0.3310.1960.9590.9520.9461.0000.914
댐이름0.0001.0000.0000.8780.8380.9950.9141.000

Missing values

2023-12-10T19:55:51.885087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:55:52.129445image/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
0강천보2019010138.070.079.86977.7749.045103.6
1강천보2019010238.160.0101.8197.0959.452108.3
2강천보2019010338.160.0110.201110.2019.452108.3
3강천보2019010438.150.0107.597108.1219.407107.8
4강천보2019010538.160.0109.422108.8989.452108.3
5강천보2019010638.120.0102.432104.5279.271106.2
6강천보2019010738.170.0110.085107.4669.497108.8
7강천보2019010838.150.0109.294110.3429.407107.8
8강천보2019010938.150.0119108.406108.4069.407107.8
9강천보2019011038.150.0108.583108.5839.407107.8
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구담보2019012962.470.00.00.00.00.0
91구담보2019013062.470.00.00.00.00.0
92구담보2019013162.470.00.00.00.00.0
93구미보2019010132.510.023535.03735.03752.798100.1
94구미보2019010232.520.036.38235.62852.863100.3
95구미보2019010332.480.023536.78540.16452.57199.7
96구미보2019010432.520.038.88435.50552.863100.3
97구미보2019010532.510.036.34937.10352.798100.1
98구미보2019010632.510.023538.07338.07352.798100.1
99구미보2019010732.520.032.90732.15352.863100.3