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 74 (74.0%) 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 10:47:06.459943
Analysis finished2023-12-10 10:47:13.495540
Duration7.04 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

2023-12-10T19:47:13.578024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:47:13.713099image/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%
Mean20220414
Minimum20220401
Maximum20220430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:13.869328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220401
5-th percentile20220402
Q120220407
median20220414
Q320220422
95-th percentile20220429
Maximum20220430
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3685931 × 10-7
Kurtosis-1.2329291
Mean20220414
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0220414 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T19:47:14.039829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20220401 4
 
4.0%
20220403 4
 
4.0%
20220404 4
 
4.0%
20220405 4
 
4.0%
20220406 4
 
4.0%
20220407 4
 
4.0%
20220408 4
 
4.0%
20220409 4
 
4.0%
20220410 4
 
4.0%
20220402 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20220401 4
4.0%
20220402 4
4.0%
20220403 4
4.0%
20220404 4
4.0%
20220405 4
4.0%
20220406 4
4.0%
20220407 4
4.0%
20220408 4
4.0%
20220409 4
4.0%
20220410 4
4.0%
ValueCountFrequency (%)
20220430 3
3.0%
20220429 3
3.0%
20220428 3
3.0%
20220427 3
3.0%
20220426 3
3.0%
20220425 3
3.0%
20220424 3
3.0%
20220423 3
3.0%
20220422 3
3.0%
20220421 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.90582
Minimum3.62
Maximum60.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:14.188753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.62
5-th percentile3.6338
Q13.705
median38.09
Q360.4
95-th percentile60.4
Maximum60.4
Range56.78
Interquartile range (IQR)56.695

Descriptive statistics

Standard deviation22.255128
Coefficient of variation (CV)0.65638077
Kurtosis-1.3489756
Mean33.90582
Median Absolute Deviation (MAD)22.31
Skewness-0.25171653
Sum3390.582
Variance495.29074
MonotonicityNot monotonic
2023-12-10T19:47:14.331167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
60.4 30
30.0%
38.08 7
 
7.0%
38.09 6
 
6.0%
38.14 4
 
4.0%
3.66 4
 
4.0%
38.12 4
 
4.0%
3.63 4
 
4.0%
3.67 4
 
4.0%
32.56 3
 
3.0%
3.68 3
 
3.0%
Other values (22) 31
31.0%
ValueCountFrequency (%)
3.62 1
 
1.0%
3.63 4
4.0%
3.634 1
 
1.0%
3.64 3
3.0%
3.642 1
 
1.0%
3.65 2
2.0%
3.66 4
4.0%
3.67 4
4.0%
3.68 3
3.0%
3.69 2
2.0%
ValueCountFrequency (%)
60.4 30
30.0%
38.16 1
 
1.0%
38.15 1
 
1.0%
38.14 4
 
4.0%
38.13 1
 
1.0%
38.12 4
 
4.0%
38.11 3
 
3.0%
38.1 2
 
2.0%
38.09 6
 
6.0%
38.08 7
 
7.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.073474
Minimum0
Maximum22.8991
Zeros74
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:14.482380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0017
95-th percentile10.800725
Maximum22.8991
Range22.8991
Interquartile range (IQR)0.0017

Descriptive statistics

Standard deviation3.816945
Coefficient of variation (CV)3.555694
Kurtosis17.72664
Mean1.073474
Median Absolute Deviation (MAD)0
Skewness4.1185435
Sum107.3474
Variance14.569069
MonotonicityNot monotonic
2023-12-10T19:47:14.646993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 74
74.0%
0.0017 2
 
2.0%
0.0119 2
 
2.0%
0.0188 1
 
1.0%
22.8991 1
 
1.0%
0.176 1
 
1.0%
0.0076 1
 
1.0%
10.7741 1
 
1.0%
0.012 1
 
1.0%
0.008 1
 
1.0%
Other values (15) 15
 
15.0%
ValueCountFrequency (%)
0.0 74
74.0%
0.0017 2
 
2.0%
0.0052 1
 
1.0%
0.0076 1
 
1.0%
0.008 1
 
1.0%
0.0119 2
 
2.0%
0.012 1
 
1.0%
0.0188 1
 
1.0%
0.0238 1
 
1.0%
0.0241 1
 
1.0%
ValueCountFrequency (%)
22.8991 1
1.0%
20.2266 1
1.0%
12.0473 1
1.0%
11.9872 1
1.0%
11.3066 1
1.0%
10.7741 1
1.0%
7.2175 1
1.0%
4.5282 1
1.0%
4.0835 1
1.0%
1.3296 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.60437
Minimum0
Maximum112.844
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:14.799286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44.005
Q384.83225
95-th percentile99.5287
Maximum112.844
Range112.844
Interquartile range (IQR)84.83225

Descriptive statistics

Standard deviation37.200293
Coefficient of variation (CV)0.78144702
Kurtosis-1.3955274
Mean47.60437
Median Absolute Deviation (MAD)42.362
Skewness-0.0245852
Sum4760.437
Variance1383.8618
MonotonicityNot monotonic
2023-12-10T19:47:15.002617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
93.356 1
 
1.0%
39.323 1
 
1.0%
39.749 1
 
1.0%
41.696 1
 
1.0%
42.682 1
 
1.0%
43.384 1
 
1.0%
45.395 1
 
1.0%
47.619 1
 
1.0%
48.04 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
38.23 1
 
1.0%
38.799 1
 
1.0%
39.099 1
 
1.0%
39.323 1
 
1.0%
39.567 1
 
1.0%
39.749 1
 
1.0%
39.871 1
 
1.0%
40.322 1
 
1.0%
40.748 1
 
1.0%
ValueCountFrequency (%)
112.844 1
1.0%
108.157 1
1.0%
102.949 1
1.0%
100.922 1
1.0%
100.321 1
1.0%
99.487 1
1.0%
98.831 1
1.0%
96.971 1
1.0%
96.907 1
1.0%
95.46 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.61726
Minimum0
Maximum113.598
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:15.193982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median43.9115
Q384.94925
95-th percentile99.90495
Maximum113.598
Range113.598
Interquartile range (IQR)84.94925

Descriptive statistics

Standard deviation37.252324
Coefficient of variation (CV)0.78232817
Kurtosis-1.3992035
Mean47.61726
Median Absolute Deviation (MAD)42.5215
Skewness-0.021698173
Sum4761.726
Variance1387.7356
MonotonicityNot monotonic
2023-12-10T19:47:15.689953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
92.308 1
 
1.0%
39.323 1
 
1.0%
39.805 1
 
1.0%
41.752 1
 
1.0%
42.793 1
 
1.0%
43.44 1
 
1.0%
45.451 1
 
1.0%
47.73 1
 
1.0%
48.229 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
38.108 1
 
1.0%
38.877 1
 
1.0%
39.155 1
 
1.0%
39.323 1
 
1.0%
39.567 1
 
1.0%
39.805 1
 
1.0%
39.849 1
 
1.0%
40.378 1
 
1.0%
40.692 1
 
1.0%
ValueCountFrequency (%)
113.598 1
1.0%
106.8 1
1.0%
103.703 1
1.0%
101.369 1
1.0%
100.398 1
1.0%
99.879 1
1.0%
98.963 1
1.0%
96.907 1
1.0%
95.984 1
1.0%
94.876 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.676381
Minimum0
Maximum53.1237
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:15.887086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.0687
Q39.1806
95-th percentile52.92905
Maximum53.1237
Range53.1237
Interquartile range (IQR)9.1806

Descriptive statistics

Standard deviation15.299509
Coefficient of variation (CV)1.7633515
Kurtosis4.4727926
Mean8.676381
Median Absolute Deviation (MAD)2.0687
Skewness2.4152188
Sum867.6381
Variance234.07499
MonotonicityNot monotonic
2023-12-10T19:47:16.082728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 30
30.0%
9.0901 7
 
7.0%
9.1353 6
 
6.0%
9.3616 4
 
4.0%
2.0615 4
 
4.0%
9.2711 4
 
4.0%
2.047 4
 
4.0%
2.0663 4
 
4.0%
53.1237 3
 
3.0%
2.0711 3
 
3.0%
Other values (22) 31
31.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.0422 1
 
1.0%
2.047 4
 
4.0%
2.049 1
 
1.0%
2.0518 3
 
3.0%
2.0528 1
 
1.0%
2.0567 2
 
2.0%
2.0615 4
 
4.0%
2.0663 4
 
4.0%
2.0711 3
 
3.0%
ValueCountFrequency (%)
53.1237 3
3.0%
52.9935 1
 
1.0%
52.9414 1
 
1.0%
52.9284 1
 
1.0%
52.8632 1
 
1.0%
52.7981 2
2.0%
52.733 1
 
1.0%
9.4522 1
 
1.0%
9.4069 1
 
1.0%
9.3616 4
4.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.683
Minimum0
Maximum108.3
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:16.268842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.3
Q3104.2
95-th percentile107.3
Maximum108.3
Range108.3
Interquartile range (IQR)104.2

Descriptive statistics

Standard deviation48.352143
Coefficient of variation (CV)1.0584275
Kurtosis-1.8387966
Mean45.683
Median Absolute Deviation (MAD)13.3
Skewness0.38233421
Sum4568.3
Variance2337.9297
MonotonicityNot monotonic
2023-12-10T19:47:16.446903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 30
30.0%
13.3 11
 
11.0%
13.2 11
 
11.0%
104.2 7
 
7.0%
104.7 6
 
6.0%
13.4 4
 
4.0%
106.2 4
 
4.0%
107.3 4
 
4.0%
100.7 3
 
3.0%
105.7 3
 
3.0%
Other values (13) 17
17.0%
ValueCountFrequency (%)
0.0 30
30.0%
13.1 1
 
1.0%
13.2 11
 
11.0%
13.3 11
 
11.0%
13.4 4
 
4.0%
13.5 2
 
2.0%
13.7 1
 
1.0%
100.0 1
 
1.0%
100.1 2
 
2.0%
100.3 1
 
1.0%
ValueCountFrequency (%)
108.3 1
 
1.0%
107.8 1
 
1.0%
107.3 4
4.0%
106.7 1
 
1.0%
106.2 4
4.0%
105.7 3
3.0%
105.2 2
 
2.0%
104.7 6
6.0%
104.2 7
7.0%
103.6 1
 
1.0%

Interactions

2023-12-10T19:47:12.397801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:06.843967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.734268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.566533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:09.429883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:10.666066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.559948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.527689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:06.972085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.868349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.686999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:09.587809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:10.824260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.695829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.650250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.098379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.983935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.794277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:09.761420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:10.962863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.826562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.776237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.246198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.091020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.894391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:09.895993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.083534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.939088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.899743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.372468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.209626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:09.015975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:10.032252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.211542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.056334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.995811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.491332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.332708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:09.156486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:10.168607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.323627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.173454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:13.091948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:07.613406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:08.459911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:09.297013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:10.538812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:11.449242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:12.285824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:47:16.563359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9240.9281.0001.000
일자/시간(t)0.0001.0000.0000.1640.3210.3460.0000.000
저수위(m)1.0000.0001.0000.0000.9240.9281.0001.000
강우량(mm)0.0000.1640.0001.0000.0000.0000.0000.060
유입량(ms)0.9240.3210.9240.0001.0000.9990.8710.944
방류량(ms)0.9280.3460.9280.0000.9991.0000.8770.949
저수량(백만m3)1.0000.0001.0000.0000.8710.8771.0000.940
저수율1.0000.0001.0000.0600.9440.9490.9401.000
2023-12-10T19:47:16.744563image/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)1.0000.0330.073-0.144-0.134-0.210-0.0840.000
저수위(m)0.0331.000-0.244-0.330-0.334-0.370-0.2991.000
강우량(mm)0.073-0.2441.0000.3520.3390.3560.3710.000
유입량(ms)-0.144-0.3300.3521.0000.9990.9470.9650.886
방류량(ms)-0.134-0.3340.3390.9991.0000.9390.9610.894
저수량(백만m3)-0.210-0.3700.3560.9470.9391.0000.9250.995
저수율-0.084-0.2990.3710.9650.9610.9251.0000.995
댐이름0.0001.0000.0000.8860.8940.9950.9951.000

Missing values

2023-12-10T19:47:13.273443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:47:13.433447image/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
0강천보2022040138.140.018893.35692.3089.3616107.3
1강천보2022040238.140.096.90796.9079.3616107.3
2강천보2022040338.120.093.41194.4599.2711106.2
3강천보2022040438.120.005291.6891.689.2711106.2
4강천보2022040538.110.023889.73390.2579.2259105.7
5강천보2022040638.090.0100.321101.3699.1353104.7
6강천보2022040738.10.027786.33185.8079.1806105.2
7강천보2022040838.090.085.17185.6959.1353104.7
8강천보2022040938.090.084.884.89.1353104.7
9강천보2022041038.080.083.51884.0429.0901104.2
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2022040132.540.0076108.157106.852.9935100.5
91구미보2022040232.530.0017102.949103.70352.9284100.4
92구미보2022040332.520.0112.844113.59852.8632100.3
93구미보2022040432.510.090.15390.90752.7981100.1
94구미보2022040532.510.083.50283.50252.7981100.1
95구미보2022040632.560.048.11244.34353.1237100.7
96구미보2022040732.50.17661.95566.47752.733100.0
97구미보2022040832.560.001755.5951.06853.1237100.7
98구미보2022040932.560.065.32865.32853.1237100.7
99구미보2022041032.5320.054.77356.88352.9414100.4