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 54 (54.0%) zerosZeros
유입량(ms) has 15 (15.0%) zerosZeros
방류량(ms) has 15 (15.0%) zerosZeros
저수량(백만m3) has 15 (15.0%) zerosZeros
저수율 has 15 (15.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:49:29.423310
Analysis finished2023-12-10 10:49:38.742278
Duration9.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
달성보
16 
구담보
15 
낙단보
14 
공주보
13 
귤현보
13 
Other values (3)
29 

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 (%)
달성보 16
16.0%
구담보 15
15.0%
낙단보 14
14.0%
공주보 13
13.0%
귤현보 13
13.0%
강천보 12
12.0%
구미보 12
12.0%
백제보 5
 
5.0%

Length

2023-12-10T19:49:38.862185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:39.069726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
달성보 16
16.0%
구담보 15
15.0%
낙단보 14
14.0%
공주보 13
13.0%
귤현보 13
13.0%
강천보 12
12.0%
구미보 12
12.0%
백제보 5
 
5.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210316
Minimum20210301
Maximum20210331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:39.673364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210301
5-th percentile20210302
Q120210308
median20210316
Q320210323
95-th percentile20210329
Maximum20210331
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7174735
Coefficient of variation (CV)4.3133781 × 10-7
Kurtosis-1.1916043
Mean20210316
Median Absolute Deviation (MAD)7.5
Skewness0.029618046
Sum2.0210316 × 109
Variance75.994343
MonotonicityNot monotonic
2023-12-10T19:49:39.945903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20210307 6
 
6.0%
20210311 5
 
5.0%
20210323 5
 
5.0%
20210305 5
 
5.0%
20210316 5
 
5.0%
20210324 4
 
4.0%
20210302 4
 
4.0%
20210320 4
 
4.0%
20210314 4
 
4.0%
20210317 4
 
4.0%
Other values (21) 54
54.0%
ValueCountFrequency (%)
20210301 2
 
2.0%
20210302 4
4.0%
20210303 2
 
2.0%
20210304 2
 
2.0%
20210305 5
5.0%
20210306 3
3.0%
20210307 6
6.0%
20210308 2
 
2.0%
20210309 3
3.0%
20210310 4
4.0%
ValueCountFrequency (%)
20210331 2
 
2.0%
20210330 3
3.0%
20210329 3
3.0%
20210328 4
4.0%
20210327 3
3.0%
20210326 2
 
2.0%
20210325 3
3.0%
20210324 4
4.0%
20210323 5
5.0%
20210322 2
 
2.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.2321
Minimum2.89
Maximum68.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:40.203524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.89
5-th percentile3.226
Q13.6775
median32.555
Q339.79
95-th percentile67.439
Maximum68.03
Range65.14
Interquartile range (IQR)36.1125

Descriptive statistics

Standard deviation21.840396
Coefficient of variation (CV)0.80200925
Kurtosis-0.9283525
Mean27.2321
Median Absolute Deviation (MAD)18.975
Skewness0.47483666
Sum2723.21
Variance477.0029
MonotonicityNot monotonic
2023-12-10T19:49:40.492980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.03 7
 
7.0%
3.66 6
 
6.0%
68.03 4
 
4.0%
13.6 3
 
3.0%
3.67 3
 
3.0%
66.64 3
 
3.0%
13.65 2
 
2.0%
13.55 2
 
2.0%
32.56 2
 
2.0%
39.89 2
 
2.0%
Other values (57) 66
66.0%
ValueCountFrequency (%)
2.89 1
1.0%
2.91 2
2.0%
2.94 1
1.0%
2.96 1
1.0%
3.24 1
1.0%
3.26 1
1.0%
3.29 1
1.0%
3.3 1
1.0%
3.31 1
1.0%
3.32 2
2.0%
ValueCountFrequency (%)
68.03 4
4.0%
67.8 1
 
1.0%
67.42 1
 
1.0%
66.64 3
3.0%
66.02 1
 
1.0%
65.24 1
 
1.0%
64.34 1
 
1.0%
64.02 1
 
1.0%
63.23 1
 
1.0%
62.45 1
 
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.606433
Minimum0
Maximum46.1558
Zeros54
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:40.776062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.060275
95-th percentile5.165025
Maximum46.1558
Range46.1558
Interquartile range (IQR)0.060275

Descriptive statistics

Standard deviation6.7873925
Coefficient of variation (CV)4.2251326
Kurtosis36.73433
Mean1.606433
Median Absolute Deviation (MAD)0
Skewness5.9398953
Sum160.6433
Variance46.068697
MonotonicityNot monotonic
2023-12-10T19:49:41.006481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 54
54.0%
0.0318 3
 
3.0%
0.0077 2
 
2.0%
0.0169 1
 
1.0%
0.0149 1
 
1.0%
0.0699 1
 
1.0%
46.1558 1
 
1.0%
0.0155 1
 
1.0%
3.7048 1
 
1.0%
0.0117 1
 
1.0%
Other values (34) 34
34.0%
ValueCountFrequency (%)
0.0 54
54.0%
0.0047 1
 
1.0%
0.0059 1
 
1.0%
0.0075 1
 
1.0%
0.0077 2
 
2.0%
0.0115 1
 
1.0%
0.0117 1
 
1.0%
0.0119 1
 
1.0%
0.0149 1
 
1.0%
0.0155 1
 
1.0%
ValueCountFrequency (%)
46.1558 1
1.0%
45.9572 1
1.0%
16.958 1
1.0%
11.9846 1
1.0%
5.1655 1
1.0%
5.165 1
1.0%
4.3351 1
1.0%
3.7048 1
1.0%
3.6479 1
1.0%
3.6197 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.9318
Minimum0
Maximum263.921
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:41.278169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.359
median49.4945
Q390.7685
95-th percentile130.6241
Maximum263.921
Range263.921
Interquartile range (IQR)77.4095

Descriptive statistics

Standard deviation47.269775
Coefficient of variation (CV)0.81595557
Kurtosis2.5229988
Mean57.9318
Median Absolute Deviation (MAD)38.6525
Skewness1.1088834
Sum5793.18
Variance2234.4316
MonotonicityNot monotonic
2023-12-10T19:49:41.550537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
85.918 1
 
1.0%
10.5 1
 
1.0%
60.935 1
 
1.0%
52.017 1
 
1.0%
51.473 1
 
1.0%
47.612 1
 
1.0%
45.593 1
 
1.0%
46.758 1
 
1.0%
48.468 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 15
15.0%
9.405 1
 
1.0%
9.818 1
 
1.0%
10.177 1
 
1.0%
10.226 1
 
1.0%
10.496 1
 
1.0%
10.5 1
 
1.0%
10.651 1
 
1.0%
10.726 1
 
1.0%
10.958 1
 
1.0%
ValueCountFrequency (%)
263.921 1
1.0%
168.964 1
1.0%
163.792 1
1.0%
153.912 1
1.0%
152.97 1
1.0%
129.448 1
1.0%
119.809 1
1.0%
115.226 1
1.0%
113.248 1
1.0%
113.153 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.31801
Minimum0
Maximum238.133
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:41.799020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.18525
median48.719
Q391.46825
95-th percentile130.4256
Maximum238.133
Range238.133
Interquartile range (IQR)77.283

Descriptive statistics

Standard deviation45.795865
Coefficient of variation (CV)0.79897863
Kurtosis1.3648234
Mean57.31801
Median Absolute Deviation (MAD)38.0195
Skewness0.90052303
Sum5731.801
Variance2097.2613
MonotonicityNot monotonic
2023-12-10T19:49:42.018098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
86.8 1
 
1.0%
10.303 1
 
1.0%
66.367 1
 
1.0%
43.189 1
 
1.0%
56.226 1
 
1.0%
48.291 1
 
1.0%
53.742 1
 
1.0%
44.042 1
 
1.0%
49.147 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 15
15.0%
9.301 1
 
1.0%
10.027 1
 
1.0%
10.073 1
 
1.0%
10.303 1
 
1.0%
10.365 1
 
1.0%
10.633 1
 
1.0%
10.761 1
 
1.0%
10.901 1
 
1.0%
11.426 1
 
1.0%
ValueCountFrequency (%)
238.133 1
1.0%
168.964 1
1.0%
153.968 1
1.0%
153.583 1
1.0%
149.0 1
1.0%
129.448 1
1.0%
123.493 1
1.0%
118.556 1
1.0%
112.565 1
1.0%
110.927 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.07364
Minimum0
Maximum58.25
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:42.261019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.989
median8.864
Q353.07525
95-th percentile54.6473
Maximum58.25
Range58.25
Interquartile range (IQR)52.08625

Descriptive statistics

Standard deviation22.746585
Coefficient of variation (CV)1.0304864
Kurtosis-1.5543479
Mean22.07364
Median Absolute Deviation (MAD)8.864
Skewness0.4574674
Sum2207.364
Variance517.40713
MonotonicityNot monotonic
2023-12-10T19:49:42.456714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
8.864 7
 
7.0%
2.061 5
 
5.0%
54.324 3
 
3.0%
2.066 3
 
3.0%
33.445 2
 
2.0%
0.8 2
 
2.0%
54.748 2
 
2.0%
53.794 2
 
2.0%
53.645 2
 
2.0%
Other values (49) 57
57.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.766 1
 
1.0%
0.775 1
 
1.0%
0.788 1
 
1.0%
0.792 1
 
1.0%
0.796 1
 
1.0%
0.8 2
 
2.0%
0.805 1
 
1.0%
0.814 1
 
1.0%
0.953 1
 
1.0%
ValueCountFrequency (%)
58.25 1
 
1.0%
54.855 2
2.0%
54.748 2
2.0%
54.642 1
 
1.0%
54.536 1
 
1.0%
54.43 1
 
1.0%
54.324 3
3.0%
54.218 1
 
1.0%
54.035 1
 
1.0%
54.006 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.7785
Minimum0
Maximum102.5
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:42.693960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median92.4
Q399.55
95-th percentile101.6
Maximum102.5
Range102.5
Interquartile range (IQR)99.45

Descriptive statistics

Standard deviation45.659876
Coefficient of variation (CV)0.7902572
Kurtosis-1.8419469
Mean57.7785
Median Absolute Deviation (MAD)9.25
Skewness-0.32325547
Sum5777.85
Variance2084.8243
MonotonicityNot monotonic
2023-12-10T19:49:42.917483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 15
 
15.0%
0.1 11
 
11.0%
13.3 9
 
9.0%
101.6 7
 
7.0%
92.8 3
 
3.0%
101.0 3
 
3.0%
91.9 2
 
2.0%
93.7 2
 
2.0%
95.8 2
 
2.0%
93.5 2
 
2.0%
Other values (35) 44
44.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.1 11
11.0%
0.2 2
 
2.0%
13.2 2
 
2.0%
13.3 9
9.0%
13.4 1
 
1.0%
13.5 1
 
1.0%
63.8 1
 
1.0%
64.2 2
 
2.0%
64.9 1
 
1.0%
ValueCountFrequency (%)
102.5 1
 
1.0%
102.1 1
 
1.0%
101.73 1
 
1.0%
101.7 1
 
1.0%
101.6 7
7.0%
101.4 2
 
2.0%
101.0 3
3.0%
100.9 1
 
1.0%
100.7 2
 
2.0%
100.6 1
 
1.0%

Interactions

2023-12-10T19:49:37.185301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:29.860372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.093379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.665254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.759356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.964131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:36.042396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:37.347119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.024919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.250451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.820997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.922328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:35.149972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:36.192544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:37.524895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.212021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.409945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.991325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.081274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:35.319780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:36.349403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:37.684013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.363758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.565213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.141959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.236374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:35.467213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:36.511557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:37.870019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.512982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.715163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.299013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.426914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:35.619550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:36.685574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:38.013845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.668216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.235408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.457584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.644039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:35.768850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:36.843368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:38.168348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.925366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.490326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.610108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.814910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:35.918735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:37.034026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:49:43.060814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.2520.8870.8051.0000.929
일자/시간(t)0.0001.0000.0000.3720.2920.0000.0000.000
저수위(m)1.0000.0001.0000.0000.6760.7650.9560.912
강우량(mm)0.2520.3720.0001.0000.2960.5090.5140.375
유입량(ms)0.8870.2920.6760.2961.0000.9410.6920.745
방류량(ms)0.8050.0000.7650.5090.9411.0000.7680.803
저수량(백만m3)1.0000.0000.9560.5140.6920.7681.0000.957
저수율0.9290.0000.9120.3750.7450.8030.9571.000
2023-12-10T19:49:43.327851image/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)1.0000.106-0.091-0.141-0.135-0.100-0.0670.000
저수위(m)0.1061.000-0.214-0.075-0.092-0.1090.1140.984
강우량(mm)-0.091-0.2141.0000.3130.3050.3470.2840.152
유입량(ms)-0.141-0.0750.3131.0000.9930.8680.8060.501
방류량(ms)-0.135-0.0920.3050.9931.0000.8580.7930.561
저수량(백만m3)-0.100-0.1090.3470.8680.8581.0000.7250.984
저수율-0.0670.1140.2840.8060.7930.7251.0000.870
댐이름0.0000.9840.1520.5010.5610.9840.8701.000

Missing values

2023-12-10T19:49:38.372298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:49:38.647483image/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
0강천보2021032537.990.085.91886.88.69799.7
1강천보2021032438.010.086.74287.2668.773100.5
2강천보2021030638.030.0119110.927110.9278.864101.6
3강천보2021032038.032.526493.25892.218.864101.6
4강천보2021031838.040.091.74591.2218.909102.1
5강천보2021030738.030.0107.218107.2188.864101.6
6강천보2021032338.020.093.0193.5348.819101.0
7강천보2021031338.030.095.48695.4868.864101.6
8강천보2021032238.030.090.44390.4438.864101.6
9강천보2021032738.0211.984675.81776.3418.819101.0
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90달성보2021030213.974.3351263.921238.13358.2599.5
91달성보2021031413.630.0047129.448129.44854.64293.3
92달성보2021031513.550.0186108.732118.55653.79491.9
93달성보2021030713.620.0104.992103.76454.53693.12
94달성보2021031613.590.0719153.912149.054.21892.6
95백제보202103032.960.0482152.97153.58315.78865.3
96백제보202103132.940.173253.29950.84515.68264.9
97백제보202103212.910.044845.35242.89815.52364.2
98백제보202103112.910.071345.33842.88415.52364.2
99백제보202103272.8916.95843.79542.56815.41763.8