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
강우량(mm) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
저수율 is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
강우량(mm) has 31 (31.0%) zerosZeros
유입량(ms) has 21 (21.0%) zerosZeros
방류량(ms) has 21 (21.0%) zerosZeros
저수량(백만m3) has 21 (21.0%) zerosZeros
저수율 has 21 (21.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:48:23.884298
Analysis finished2023-12-10 10:48:31.459069
Duration7.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
구담보
21 
공주보
19 
강천보
18 
구미보
14 
귤현보
13 
Other values (2)
15 

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 (%)
구담보 21
21.0%
공주보 19
19.0%
강천보 18
18.0%
구미보 14
14.0%
귤현보 13
13.0%
낙단보 11
11.0%
달성보 4
 
4.0%

Length

2023-12-10T19:48:31.552802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:48:31.689617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구담보 21
21.0%
공주보 19
19.0%
강천보 18
18.0%
구미보 14
14.0%
귤현보 13
13.0%
낙단보 11
11.0%
달성보 4
 
4.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210817
Minimum20210801
Maximum20210831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:31.845325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210801
5-th percentile20210802
Q120210810
median20210817
Q320210825
95-th percentile20210829
Maximum20210831
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8461828
Coefficient of variation (CV)4.3769546 × 10-7
Kurtosis-1.1563196
Mean20210817
Median Absolute Deviation (MAD)7
Skewness-0.1014757
Sum2.0210817 × 109
Variance78.254949
MonotonicityNot monotonic
2023-12-10T19:48:32.018140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20210828 6
 
6.0%
20210812 5
 
5.0%
20210813 5
 
5.0%
20210827 5
 
5.0%
20210826 4
 
4.0%
20210814 4
 
4.0%
20210806 4
 
4.0%
20210821 4
 
4.0%
20210818 4
 
4.0%
20210822 4
 
4.0%
Other values (21) 55
55.0%
ValueCountFrequency (%)
20210801 3
3.0%
20210802 3
3.0%
20210803 2
2.0%
20210804 3
3.0%
20210805 2
2.0%
20210806 4
4.0%
20210807 3
3.0%
20210808 2
2.0%
20210809 1
 
1.0%
20210810 4
4.0%
ValueCountFrequency (%)
20210831 3
3.0%
20210830 2
 
2.0%
20210829 4
4.0%
20210828 6
6.0%
20210827 5
5.0%
20210826 4
4.0%
20210825 2
 
2.0%
20210824 2
 
2.0%
20210823 2
 
2.0%
20210822 4
4.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.3381
Minimum3.22
Maximum60.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:32.256160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.22
5-th percentile3.603
Q14.3625
median35.45
Q339.7825
95-th percentile60.4
Maximum60.4
Range57.18
Interquartile range (IQR)35.42

Descriptive statistics

Standard deviation21.176128
Coefficient of variation (CV)0.69800442
Kurtosis-1.3326354
Mean30.3381
Median Absolute Deviation (MAD)24.95
Skewness-0.014227866
Sum3033.81
Variance448.42839
MonotonicityNot monotonic
2023-12-10T19:48:32.435068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.4 21
 
21.0%
3.72 3
 
3.0%
38.1 3
 
3.0%
3.73 3
 
3.0%
38.13 2
 
2.0%
39.78 2
 
2.0%
32.59 2
 
2.0%
32.65 2
 
2.0%
32.61 2
 
2.0%
3.74 2
 
2.0%
Other values (54) 58
58.0%
ValueCountFrequency (%)
3.22 1
1.0%
3.29 1
1.0%
3.35 1
1.0%
3.41 1
1.0%
3.47 1
1.0%
3.61 1
1.0%
3.67 1
1.0%
3.68 1
1.0%
3.7 1
1.0%
3.71 1
1.0%
ValueCountFrequency (%)
60.4 21
21.0%
39.93 1
 
1.0%
39.89 1
 
1.0%
39.85 1
 
1.0%
39.79 1
 
1.0%
39.78 2
 
2.0%
39.76 1
 
1.0%
39.75 1
 
1.0%
39.72 1
 
1.0%
39.71 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.891138
Minimum0
Maximum61
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:32.625293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2795
Q35.091775
95-th percentile27.343415
Maximum61
Range61
Interquartile range (IQR)5.091775

Descriptive statistics

Standard deviation11.720976
Coefficient of variation (CV)1.9895945
Kurtosis8.6261659
Mean5.891138
Median Absolute Deviation (MAD)0.2795
Skewness2.8342709
Sum589.1138
Variance137.38127
MonotonicityNot monotonic
2023-12-10T19:48:32.813093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
0.0387 2
 
2.0%
0.1058 1
 
1.0%
5.0 1
 
1.0%
61.0 1
 
1.0%
0.8843 1
 
1.0%
3.1043 1
 
1.0%
46.7002 1
 
1.0%
0.9145 1
 
1.0%
0.3765 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 31
31.0%
0.0059 1
 
1.0%
0.0104 1
 
1.0%
0.0149 1
 
1.0%
0.0181 1
 
1.0%
0.0224 1
 
1.0%
0.0278 1
 
1.0%
0.0339 1
 
1.0%
0.038 1
 
1.0%
0.0387 2
 
2.0%
ValueCountFrequency (%)
61.0 1
1.0%
55.1153 1
1.0%
46.7002 1
1.0%
40.915 1
1.0%
32.4756 1
1.0%
27.0733 1
1.0%
26.0385 1
1.0%
25.5334 1
1.0%
21.7384 1
1.0%
17.7035 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.3692
Minimum0
Maximum1280.085
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:33.016350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.0085
median99.734
Q3163.37025
95-th percentile654.79075
Maximum1280.085
Range1280.085
Interquartile range (IQR)151.36175

Descriptive statistics

Standard deviation234.6657
Coefficient of variation (CV)1.4190411
Kurtosis5.753723
Mean165.3692
Median Absolute Deviation (MAD)86.9465
Skewness2.2873115
Sum16536.92
Variance55067.99
MonotonicityNot monotonic
2023-12-10T19:48:33.251959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
21.0%
148.82 1
 
1.0%
119.254 1
 
1.0%
12.833 1
 
1.0%
13.097 1
 
1.0%
12.137 1
 
1.0%
12.605 1
 
1.0%
11.623 1
 
1.0%
14.232 1
 
1.0%
55.504 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 21
21.0%
10.071 1
 
1.0%
10.692 1
 
1.0%
10.793 1
 
1.0%
11.623 1
 
1.0%
12.137 1
 
1.0%
12.605 1
 
1.0%
12.742 1
 
1.0%
12.833 1
 
1.0%
13.097 1
 
1.0%
ValueCountFrequency (%)
1280.085 1
1.0%
924.81 1
1.0%
785.796 1
1.0%
718.301 1
1.0%
702.077 1
1.0%
652.302 1
1.0%
644.564 1
1.0%
622.187 1
1.0%
608.79 1
1.0%
583.168 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.95825
Minimum0
Maximum1301.026
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:33.460511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.89
median95.9955
Q3160.03775
95-th percentile640.49705
Maximum1301.026
Range1301.026
Interquartile range (IQR)148.14775

Descriptive statistics

Standard deviation234.47783
Coefficient of variation (CV)1.4214374
Kurtosis6.1248947
Mean164.95825
Median Absolute Deviation (MAD)82.2665
Skewness2.3302843
Sum16495.825
Variance54979.85
MonotonicityNot monotonic
2023-12-10T19:48:33.689863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
21.0%
152.487 1
 
1.0%
123.776 1
 
1.0%
13.539 1
 
1.0%
13.919 1
 
1.0%
11.929 1
 
1.0%
15.012 1
 
1.0%
11.773 1
 
1.0%
15.876 1
 
1.0%
52.576 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 21
21.0%
10.418 1
 
1.0%
10.993 1
 
1.0%
11.453 1
 
1.0%
11.773 1
 
1.0%
11.929 1
 
1.0%
13.539 1
 
1.0%
13.919 1
 
1.0%
14.015 1
 
1.0%
15.012 1
 
1.0%
ValueCountFrequency (%)
1301.026 1
1.0%
918.78 1
1.0%
787.322 1
1.0%
725.669 1
1.0%
704.338 1
1.0%
637.137 1
1.0%
622.818 1
1.0%
602.65 1
1.0%
595.534 1
1.0%
584.949 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.70717
Minimum0
Maximum55.173
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:33.908351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.86025
median3.1585
Q333.22475
95-th percentile53.8589
Maximum55.173
Range55.173
Interquartile range (IQR)32.3645

Descriptive statistics

Standard deviation20.471675
Coefficient of variation (CV)1.3033331
Kurtosis-0.56120576
Mean15.70717
Median Absolute Deviation (MAD)3.1585
Skewness1.0629774
Sum1570.717
Variance419.08947
MonotonicityNot monotonic
2023-12-10T19:48:34.357301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
21.0%
2.09 3
 
3.0%
9.181 3
 
3.0%
2.095 3
 
3.0%
9.316 2
 
2.0%
33.386 2
 
2.0%
53.124 2
 
2.0%
53.71 2
 
2.0%
53.449 2
 
2.0%
53.319 2
 
2.0%
Other values (55) 58
58.0%
ValueCountFrequency (%)
0.0 21
21.0%
0.758 1
 
1.0%
0.788 1
 
1.0%
0.814 1
 
1.0%
0.84 1
 
1.0%
0.867 1
 
1.0%
0.93 1
 
1.0%
0.958 1
 
1.0%
0.963 1
 
1.0%
0.991 1
 
1.0%
ValueCountFrequency (%)
55.173 1
1.0%
54.855 1
1.0%
54.687 1
1.0%
54.536 1
1.0%
54.218 1
1.0%
53.84 1
1.0%
53.71 2
2.0%
53.645 1
1.0%
53.58 1
1.0%
53.449 2
2.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.4662
Minimum0
Maximum117.1
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:34.554285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median20.3
Q3101.4
95-th percentile109.355
Maximum117.1
Range117.1
Interquartile range (IQR)101.3

Descriptive statistics

Standard deviation48.953763
Coefficient of variation (CV)0.95118277
Kurtosis-1.9415358
Mean51.4662
Median Absolute Deviation (MAD)20.3
Skewness0.090643671
Sum5146.62
Variance2396.4709
MonotonicityNot monotonic
2023-12-10T19:48:34.729212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
21.0%
0.1 9
 
9.0%
13.5 8
 
8.0%
0.2 4
 
4.0%
105.2 3
 
3.0%
106.7 2
 
2.0%
13.4 2
 
2.0%
96.3 2
 
2.0%
101.1 2
 
2.0%
100.7 2
 
2.0%
Other values (40) 45
45.0%
ValueCountFrequency (%)
0.0 21
21.0%
0.1 9
9.0%
0.2 4
 
4.0%
13.4 2
 
2.0%
13.5 8
 
8.0%
13.6 1
 
1.0%
13.7 1
 
1.0%
14.4 1
 
1.0%
18.0 1
 
1.0%
18.1 1
 
1.0%
ValueCountFrequency (%)
117.1 2
2.0%
116.6 1
1.0%
111.9 1
1.0%
110.4 1
1.0%
109.3 1
1.0%
108.82 1
1.0%
108.3 1
1.0%
107.8 1
1.0%
106.7 2
2.0%
106.2 2
2.0%

Interactions

2023-12-10T19:48:30.246797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:24.501444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.448526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.342842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:27.201786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.172418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:29.109432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:30.373573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:24.638656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.585774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.487130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:27.370785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.307422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:29.272077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:30.481302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:24.784571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.682846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.596767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:27.507419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.482903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:29.398180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:30.591156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:24.923549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.810504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.694136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:27.636785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.624623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:29.512749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:30.799836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.049058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.942625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.809102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:27.788901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.739161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:29.621461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:30.926611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.178694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.074753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.934192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:27.911717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.869646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:29.768041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:31.021535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:25.321170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:26.195493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:27.064102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.039710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:28.997854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:30.137078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:48:34.889412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0020.6060.6560.9000.965
일자/시간(t)0.0001.0000.0000.4300.5070.5070.0000.000
저수위(m)1.0000.0001.0000.0000.6260.6710.9490.816
강우량(mm)0.0020.4300.0001.0000.8640.8370.2760.302
유입량(ms)0.6060.5070.6260.8641.0000.9970.7140.783
방류량(ms)0.6560.5070.6710.8370.9971.0000.7070.795
저수량(백만m3)0.9000.0000.9490.2760.7140.7071.0000.776
저수율0.9650.0000.8160.3020.7830.7950.7761.000
2023-12-10T19:48:35.085262image/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)1.000-0.1170.1160.3310.3320.1390.0350.000
저수위(m)-0.1171.000-0.288-0.191-0.192-0.165-0.1040.989
강우량(mm)0.116-0.2881.0000.6680.6590.6140.5480.000
유입량(ms)0.331-0.1910.6681.0000.9990.8340.8190.372
방류량(ms)0.332-0.1920.6590.9991.0000.8320.8220.417
저수량(백만m3)0.139-0.1650.6140.8340.8321.0000.8340.837
저수율0.035-0.1040.5480.8190.8220.8341.0000.711
댐이름0.0000.9890.0000.3720.4170.8370.7111.000

Missing values

2023-12-10T19:48:31.186313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:48:31.388033image/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
0강천보2021082238.130.8654148.82152.4879.316106.7
1강천보2021081638.10.0137.183137.1839.181105.2
2강천보2021080338.22.9979189.286189.819.633110.4
3강천보2021081738.10.0387136.519136.5199.181105.2
4강천보2021082038.10.1734139.357139.8819.181105.2
5강천보2021080738.180.0971155.625155.6259.543109.3
6강천보2021080838.172.6447153.032153.5569.497108.82
7강천보2021082938.3312.4652219.784219.2610.222117.1
8강천보2021082738.3310.6325255.173244.69610.222117.1
9강천보2021081038.154.0986145.857145.8579.407107.8
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90낙단보2021082539.7816.78411280.0851301.02633.38696.3
91낙단보2021082239.70.1068152.327153.68532.91794.9
92낙단보2021081639.710.0181103.97108.72332.97695.1
93낙단보2021081839.762.3225107.049115.19833.26995.9
94낙단보2021080239.9314.7534146.534137.70634.26698.8
95낙단보2021083139.726.4957281.258291.44433.03495.3
96달성보2021082013.650.1047102.6195.24254.85593.7
97달성보2021082813.620.038718.301725.66954.53693.1
98달성보2021082713.6817.1921608.79602.6555.17394.2
99달성보2021083013.590.0588492.435497.34754.21892.6