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 유입량(ms) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 4 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 3 other fieldsHigh correlation
강우량(mm) has 77 (77.0%) zerosZeros
유입량(ms) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:20:50.971960
Analysis finished2023-12-10 13:20:59.729609
Duration8.76 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
군위
31 
남강
31 
대청
31 
밀양
7 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군위 31
31.0%
남강 31
31.0%
대청 31
31.0%
밀양 7
 
7.0%

Length

2023-12-10T22:20:59.942365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:00.192248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위 31
31.0%
남강 31
31.0%
대청 31
31.0%
밀양 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190515
Minimum20190501
Maximum20190531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:00.395519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190501
5-th percentile20190502
Q120190507
median20190515
Q320190523
95-th percentile20190530
Maximum20190531
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5636805 × 10-7
Kurtosis-1.2602055
Mean20190515
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0190515 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T22:21:00.612855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190501 4
 
4.0%
20190503 4
 
4.0%
20190504 4
 
4.0%
20190505 4
 
4.0%
20190506 4
 
4.0%
20190507 4
 
4.0%
20190502 4
 
4.0%
20190526 3
 
3.0%
20190522 3
 
3.0%
20190523 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190501 4
4.0%
20190502 4
4.0%
20190503 4
4.0%
20190504 4
4.0%
20190505 4
4.0%
20190506 4
4.0%
20190507 4
4.0%
20190508 3
3.0%
20190509 3
3.0%
20190510 3
3.0%
ValueCountFrequency (%)
20190531 3
3.0%
20190530 3
3.0%
20190529 3
3.0%
20190528 3
3.0%
20190527 3
3.0%
20190526 3
3.0%
20190525 3
3.0%
20190524 3
3.0%
20190523 3
3.0%
20190522 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.476
Minimum39.61
Maximum196.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:01.314910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39.61
5-th percentile39.7085
Q140.3125
median74.05
Q3191.53
95-th percentile196.6915
Maximum196.77
Range157.16
Interquartile range (IQR)151.2175

Descriptive statistics

Standard deviation67.551191
Coefficient of variation (CV)0.62272937
Kurtosis-1.7340108
Mean108.476
Median Absolute Deviation (MAD)34.2
Skewness0.38246545
Sum10847.6
Variance4563.1634
MonotonicityNot monotonic
2023-12-10T22:21:01.646077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192.35 2
 
2.0%
40.08 2
 
2.0%
192.38 2
 
2.0%
196.76 2
 
2.0%
74.56 2
 
2.0%
39.68 2
 
2.0%
39.73 2
 
2.0%
39.75 2
 
2.0%
39.9 2
 
2.0%
74.09 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
39.61 1
1.0%
39.63 1
1.0%
39.66 1
1.0%
39.68 2
2.0%
39.71 1
1.0%
39.72 1
1.0%
39.73 2
2.0%
39.75 2
2.0%
39.8 1
1.0%
39.9 2
2.0%
ValueCountFrequency (%)
196.77 1
1.0%
196.76 2
2.0%
196.73 1
1.0%
196.72 1
1.0%
196.69 1
1.0%
196.63 1
1.0%
192.38 2
2.0%
192.37 1
1.0%
192.35 2
2.0%
192.3 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.917696
Minimum0
Maximum48.5262
Zeros77
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:01.895945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15.598985
Maximum48.5262
Range48.5262
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.163395
Coefficient of variation (CV)3.7354174
Kurtosis25.663422
Mean1.917696
Median Absolute Deviation (MAD)0
Skewness4.8618469
Sum191.7696
Variance51.314229
MonotonicityNot monotonic
2023-12-10T22:21:02.109880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 77
77.0%
1.0 2
 
2.0%
0.0451 1
 
1.0%
0.242 1
 
1.0%
0.2841 1
 
1.0%
0.1075 1
 
1.0%
15.5104 1
 
1.0%
2.1205 1
 
1.0%
2.056 1
 
1.0%
19.1411 1
 
1.0%
Other values (13) 13
 
13.0%
ValueCountFrequency (%)
0.0 77
77.0%
0.0451 1
 
1.0%
0.057 1
 
1.0%
0.0721 1
 
1.0%
0.1075 1
 
1.0%
0.242 1
 
1.0%
0.2841 1
 
1.0%
0.7634 1
 
1.0%
0.9914 1
 
1.0%
1.0 2
 
2.0%
ValueCountFrequency (%)
48.5262 1
1.0%
39.5233 1
1.0%
21.7097 1
1.0%
19.1411 1
1.0%
17.2821 1
1.0%
15.5104 1
1.0%
9.7633 1
1.0%
5.668 1
1.0%
3.2366 1
1.0%
2.1205 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.89821
Minimum0
Maximum128.196
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:02.380056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08455
Q10.62475
median15.087
Q325.107
95-th percentile92.01315
Maximum128.196
Range128.196
Interquartile range (IQR)24.48225

Descriptive statistics

Standard deviation28.754925
Coefficient of variation (CV)1.3131176
Kurtosis3.9233503
Mean21.89821
Median Absolute Deviation (MAD)14.2075
Skewness2.0119001
Sum2189.821
Variance826.84574
MonotonicityNot monotonic
2023-12-10T22:21:02.620702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.09 2
 
2.0%
2.783 1
 
1.0%
17.867 1
 
1.0%
10.642 1
 
1.0%
24.446 1
 
1.0%
17.474 1
 
1.0%
17.125 1
 
1.0%
22.612 1
 
1.0%
22.266 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.089 1
 
1.0%
0.09 2
 
2.0%
0.107 1
 
1.0%
0.109 1
 
1.0%
0.118 1
 
1.0%
0.119 1
 
1.0%
0.12 1
 
1.0%
0.122 1
 
1.0%
0.123 1
 
1.0%
ValueCountFrequency (%)
128.196 1
1.0%
118.008 1
1.0%
115.742 1
1.0%
108.856 1
1.0%
98.21 1
1.0%
91.687 1
1.0%
88.844 1
1.0%
68.556 1
1.0%
61.016 1
1.0%
58.988 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.92799
Minimum1.102
Maximum104.541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:02.854174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.102
5-th percentile1.111
Q11.43425
median46.408
Q365.05675
95-th percentile78.3081
Maximum104.541
Range103.439
Interquartile range (IQR)63.6225

Descriptive statistics

Standard deviation32.056703
Coefficient of variation (CV)0.89224872
Kurtosis-1.5198001
Mean35.92799
Median Absolute Deviation (MAD)29.337
Skewness0.1452869
Sum3592.799
Variance1027.6322
MonotonicityNot monotonic
2023-12-10T22:21:03.157804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.111 8
 
8.0%
1.139 2
 
2.0%
1.151 2
 
2.0%
1.134 1
 
1.0%
63.85 1
 
1.0%
65.14 1
 
1.0%
64.552 1
 
1.0%
65.713 1
 
1.0%
65.464 1
 
1.0%
65.662 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
1.102 1
 
1.0%
1.111 8
8.0%
1.115 1
 
1.0%
1.116 1
 
1.0%
1.121 1
 
1.0%
1.123 1
 
1.0%
1.124 1
 
1.0%
1.125 1
 
1.0%
1.134 1
 
1.0%
1.136 1
 
1.0%
ValueCountFrequency (%)
104.541 1
1.0%
104.077 1
1.0%
96.659 1
1.0%
79.488 1
1.0%
78.861 1
1.0%
78.279 1
1.0%
78.254 1
1.0%
74.872 1
1.0%
74.763 1
1.0%
74.394 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369.55542
Minimum19.801
Maximum1049.623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:03.462463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.801
5-th percentile20.2216
Q122.21
median151.0155
Q3965.83475
95-th percentile1043.7582
Maximum1049.623
Range1029.822
Interquartile range (IQR)943.62475

Descriptive statistics

Standard deviation431.07779
Coefficient of variation (CV)1.1664767
Kurtosis-1.3239645
Mean369.55542
Median Absolute Deviation (MAD)129.5825
Skewness0.78875603
Sum36955.542
Variance185828.06
MonotonicityNot monotonic
2023-12-10T22:21:03.710931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.317 2
 
2.0%
157.115 2
 
2.0%
22.37 2
 
2.0%
50.491 2
 
2.0%
1049.623 2
 
2.0%
146.572 2
 
2.0%
147.872 2
 
2.0%
148.393 2
 
2.0%
152.33 2
 
2.0%
1021.531 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
19.801 1
1.0%
19.903 1
1.0%
19.987 1
1.0%
20.072 1
1.0%
20.157 1
1.0%
20.225 1
1.0%
20.327 1
1.0%
20.43 1
1.0%
20.515 1
1.0%
20.601 1
1.0%
ValueCountFrequency (%)
1049.623 2
2.0%
1048.418 1
1.0%
1047.214 1
1.0%
1046.613 1
1.0%
1043.608 1
1.0%
1040.608 1
1.0%
1037.016 1
1.0%
1033.43 1
1.0%
1029.256 1
1.0%
1025.092 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.773
Minimum40.7
Maximum70.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:03.935489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.7
5-th percentile41.495
Q145.575
median51.1
Q366.75
95-th percentile70.01
Maximum70.4
Range29.7
Interquartile range (IQR)21.175

Descriptive statistics

Standard deviation10.68261
Coefficient of variation (CV)0.19503423
Kurtosis-1.6181394
Mean54.773
Median Absolute Deviation (MAD)8.35
Skewness0.27484721
Sum5477.3
Variance114.11815
MonotonicityNot monotonic
2023-12-10T22:21:04.270012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.6 4
 
4.0%
70.4 3
 
3.0%
45.9 3
 
3.0%
45.8 2
 
2.0%
47.4 2
 
2.0%
50.8 2
 
2.0%
49.3 2
 
2.0%
48.0 2
 
2.0%
47.7 2
 
2.0%
47.8 2
 
2.0%
Other values (75) 76
76.0%
ValueCountFrequency (%)
40.7 1
1.0%
40.9 1
1.0%
41.0 1
1.0%
41.2 1
1.0%
41.4 1
1.0%
41.5 1
1.0%
41.7 1
1.0%
42.0 1
1.0%
42.1 1
1.0%
42.3 1
1.0%
ValueCountFrequency (%)
70.4 3
3.0%
70.3 1
 
1.0%
70.2 1
 
1.0%
70.0 1
 
1.0%
69.8 1
 
1.0%
69.6 1
 
1.0%
69.4 1
 
1.0%
69.1 1
 
1.0%
68.8 1
 
1.0%
68.6 4
4.0%

Interactions

2023-12-10T22:20:58.174839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:51.322537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:52.791576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.022630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.971908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:56.013566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:57.026924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:58.315430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:51.628799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:52.942991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.160724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:55.130480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:56.165060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:57.164794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:58.457668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:52.144013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:53.103150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.303931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:55.298270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:56.322288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:57.362420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:58.587275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:52.265018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:53.260361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.426058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:55.438316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:56.463759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:57.556684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:58.720654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:52.392228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:53.426379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.567711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:55.573301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:56.615476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:57.705334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:58.852806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:52.518669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:53.579578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.702320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:55.690794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:56.755803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:57.846757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:59.048263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:52.651638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:53.868409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:54.847198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:55.833285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:56.903906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:58.005436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:04.527912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.6520.9710.9810.906
일자/시간(t)0.0001.0000.0000.2970.0000.4600.0000.554
저수위(m)1.0000.0001.0000.0720.7320.9541.0000.996
강우량(mm)0.0000.2970.0721.0000.4490.0000.0000.000
유입량(ms)0.6520.0000.7320.4491.0000.7220.6580.644
방류량(ms)0.9710.4600.9540.0000.7221.0000.9720.829
저수량(백만m3)0.9810.0001.0000.0000.6580.9721.0000.879
저수율0.9060.5540.9960.0000.6440.8290.8791.000
2023-12-10T22:21:04.768701image/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)1.000-0.3470.390-0.053-0.086-0.145-0.3380.000
저수위(m)-0.3471.000-0.200-0.623-0.581-0.414-0.1130.995
강우량(mm)0.390-0.2001.0000.140-0.131-0.059-0.1150.000
유입량(ms)-0.053-0.6230.1401.0000.7450.7900.6450.437
방류량(ms)-0.086-0.581-0.1310.7451.0000.8330.6670.757
저수량(백만m3)-0.145-0.414-0.0590.7900.8331.0000.8790.810
저수율-0.338-0.113-0.1150.6450.6670.8791.0000.813
댐이름0.0000.9950.0000.4370.7570.8100.8131.000

Missing values

2023-12-10T22:20:59.231589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:20:59.571347image/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
0군위20190501192.350.02.7831.13422.31745.8
1군위20190502192.380.01.7561.13622.3745.9
2군위20190503192.380.01.1241.12422.3745.9
3군위20190504192.370.00.9321.13922.35245.9
4군위20190505192.350.00.7471.1622.31745.8
5군위20190506192.30.00.1191.15122.22845.6
6군위20190507192.260.00.3271.15122.15645.5
7군위20190508192.20.00.2221.45522.0545.3
8군위20190509192.120.00.091.7321.90845.0
9군위20190510192.050.00.2931.72421.78544.7
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90대청2019052972.930.012.52365.327954.11764.0
91대청2019053072.860.107518.96165.043950.13663.8
92대청2019053172.790.284118.99864.967946.16463.5
93밀양20190501196.630.2425.9131.47950.27368.3
94밀양20190502196.720.03.2741.53350.42468.5
95밀양20190503196.760.02.3491.57450.49168.6
96밀양20190504196.770.01.7451.55150.50768.6
97밀양20190505196.760.01.3461.5450.49168.6
98밀양20190506196.730.00.9911.57250.44168.5
99밀양20190507196.690.00.8271.60150.37468.4