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 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 2 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 2 other fieldsHigh correlation
강우량(mm) has 61 (61.0%) zerosZeros
유입량(ms) has 14 (14.0%) zerosZeros
방류량(ms) has 14 (14.0%) zerosZeros
저수량(백만m3) has 14 (14.0%) zerosZeros
저수율 has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:49:14.752167
Analysis finished2023-12-10 10:49:23.170383
Duration8.42 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
강천보
15 
구미보
15 
낙단보
15 
구담보
14 
귤현보
13 
Other values (3)
28 

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 15
15.0%
구미보 15
15.0%
낙단보 15
15.0%
구담보 14
14.0%
귤현보 13
13.0%
공주보 12
12.0%
달성보 12
12.0%
백제보 4
 
4.0%

Length

2023-12-10T19:49:23.283470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:23.508337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 15
15.0%
구미보 15
15.0%
낙단보 15
15.0%
구담보 14
14.0%
귤현보 13
13.0%
공주보 12
12.0%
달성보 12
12.0%
백제보 4
 
4.0%

일자/시간(t)
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210416
Minimum20210401
Maximum20210430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:23.822337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210401
5-th percentile20210402
Q120210408
median20210416
Q320210424
95-th percentile20210429
Maximum20210430
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.0033608
Coefficient of variation (CV)4.4548122 × 10-7
Kurtosis-1.2886154
Mean20210416
Median Absolute Deviation (MAD)8
Skewness-0.098376789
Sum2.0210416 × 109
Variance81.060505
MonotonicityNot monotonic
2023-12-10T19:49:24.066184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20210427 7
 
7.0%
20210418 6
 
6.0%
20210424 5
 
5.0%
20210428 5
 
5.0%
20210419 5
 
5.0%
20210403 5
 
5.0%
20210416 5
 
5.0%
20210426 5
 
5.0%
20210408 5
 
5.0%
20210407 4
 
4.0%
Other values (19) 48
48.0%
ValueCountFrequency (%)
20210401 3
3.0%
20210402 4
4.0%
20210403 5
5.0%
20210404 4
4.0%
20210406 3
3.0%
20210407 4
4.0%
20210408 5
5.0%
20210409 1
 
1.0%
20210410 4
4.0%
20210411 3
3.0%
ValueCountFrequency (%)
20210430 2
 
2.0%
20210429 4
4.0%
20210428 5
5.0%
20210427 7
7.0%
20210426 5
5.0%
20210425 1
 
1.0%
20210424 5
5.0%
20210423 4
4.0%
20210422 1
 
1.0%
20210421 1
 
1.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.799
Minimum1.98
Maximum65.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:24.325071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.98
5-th percentile3.3295
Q14.0075
median32.58
Q339.75
95-th percentile60.4
Maximum65.24
Range63.26
Interquartile range (IQR)35.7425

Descriptive statistics

Standard deviation19.824784
Coefficient of variation (CV)0.71314737
Kurtosis-1.1378936
Mean27.799
Median Absolute Deviation (MAD)18.85
Skewness0.15094523
Sum2779.9
Variance393.02205
MonotonicityNot monotonic
2023-12-10T19:49:24.889994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.4 12
 
12.0%
38.14 3
 
3.0%
39.84 3
 
3.0%
32.51 3
 
3.0%
38.03 2
 
2.0%
32.58 2
 
2.0%
32.6 2
 
2.0%
13.59 2
 
2.0%
32.52 2
 
2.0%
3.95 2
 
2.0%
Other values (61) 67
67.0%
ValueCountFrequency (%)
1.98 1
1.0%
2.08 1
1.0%
2.47 1
1.0%
2.5 1
1.0%
3.32 1
1.0%
3.33 2
2.0%
3.45 1
1.0%
3.51 1
1.0%
3.63 1
1.0%
3.64 1
1.0%
ValueCountFrequency (%)
65.24 1
 
1.0%
63.85 1
 
1.0%
60.4 12
12.0%
39.94 1
 
1.0%
39.89 1
 
1.0%
39.86 1
 
1.0%
39.84 3
 
3.0%
39.81 2
 
2.0%
39.79 1
 
1.0%
39.77 1
 
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.142447
Minimum0
Maximum30
Zeros61
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:25.146734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04965
95-th percentile17.668075
Maximum30
Range30
Interquartile range (IQR)0.04965

Descriptive statistics

Standard deviation6.2863706
Coefficient of variation (CV)2.9342012
Kurtosis9.8984425
Mean2.142447
Median Absolute Deviation (MAD)0
Skewness3.2613074
Sum214.2447
Variance39.518456
MonotonicityNot monotonic
2023-12-10T19:49:25.400520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 61
61.0%
4.0 2
 
2.0%
0.2613 1
 
1.0%
22.4118 1
 
1.0%
0.0078 1
 
1.0%
0.0278 1
 
1.0%
0.0256 1
 
1.0%
0.0758 1
 
1.0%
0.452 1
 
1.0%
0.0283 1
 
1.0%
Other values (29) 29
29.0%
ValueCountFrequency (%)
0.0 61
61.0%
0.0076 1
 
1.0%
0.0078 1
 
1.0%
0.0115 1
 
1.0%
0.0143 1
 
1.0%
0.016 1
 
1.0%
0.0162 1
 
1.0%
0.0234 1
 
1.0%
0.0241 1
 
1.0%
0.025 1
 
1.0%
ValueCountFrequency (%)
30.0 1
1.0%
28.7072 1
1.0%
26.215 1
1.0%
22.4118 1
1.0%
22.3967 1
1.0%
17.4192 1
1.0%
16.5727 1
1.0%
12.5853 1
1.0%
10.8213 1
1.0%
5.9613 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.74137
Minimum0
Maximum522.417
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:25.621115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120.156
median72.9995
Q3116.3395
95-th percentile222.4963
Maximum522.417
Range522.417
Interquartile range (IQR)96.1835

Descriptive statistics

Standard deviation82.38447
Coefficient of variation (CV)0.98379654
Kurtosis8.2590506
Mean83.74137
Median Absolute Deviation (MAD)47.987
Skewness2.2470869
Sum8374.137
Variance6787.2009
MonotonicityNot monotonic
2023-12-10T19:49:25.906999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
73.614 1
 
1.0%
12.018 1
 
1.0%
115.061 1
 
1.0%
123.765 1
 
1.0%
90.58 1
 
1.0%
83.02 1
 
1.0%
86.383 1
 
1.0%
85.426 1
 
1.0%
101.216 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 14
14.0%
10.269 1
 
1.0%
10.715 1
 
1.0%
10.824 1
 
1.0%
11.687 1
 
1.0%
12.018 1
 
1.0%
13.43 1
 
1.0%
13.871 1
 
1.0%
14.487 1
 
1.0%
14.678 1
 
1.0%
ValueCountFrequency (%)
522.417 1
1.0%
348.114 1
1.0%
327.221 1
1.0%
243.241 1
1.0%
230.273 1
1.0%
222.087 1
1.0%
209.438 1
1.0%
208.211 1
1.0%
183.001 1
1.0%
158.014 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.71074
Minimum0
Maximum499.085
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:26.185629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.556
median72.0105
Q3118.01
95-th percentile227.8375
Maximum499.085
Range499.085
Interquartile range (IQR)101.454

Descriptive statistics

Standard deviation81.149321
Coefficient of variation (CV)0.96940155
Kurtosis7.1465195
Mean83.71074
Median Absolute Deviation (MAD)50.023
Skewness2.0955766
Sum8371.074
Variance6585.2123
MonotonicityNot monotonic
2023-12-10T19:49:26.444133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
72.042 1
 
1.0%
12.33 1
 
1.0%
121.172 1
 
1.0%
130.556 1
 
1.0%
96.691 1
 
1.0%
85.736 1
 
1.0%
88.42 1
 
1.0%
80.673 1
 
1.0%
108.007 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 14
14.0%
10.419 1
 
1.0%
10.715 1
 
1.0%
11.437 1
 
1.0%
11.999 1
 
1.0%
12.33 1
 
1.0%
14.09 1
 
1.0%
14.267 1
 
1.0%
14.312 1
 
1.0%
14.542 1
 
1.0%
ValueCountFrequency (%)
499.085 1
1.0%
340.644 1
1.0%
335.512 1
1.0%
243.241 1
1.0%
228.227 1
1.0%
227.817 1
1.0%
204.527 1
1.0%
193.723 1
1.0%
183.001 1
1.0%
162.967 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.91122
Minimum0
Maximum58.662
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:26.718472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.20875
median9.362
Q352.798
95-th percentile55.6076
Maximum58.662
Range58.662
Interquartile range (IQR)51.58925

Descriptive statistics

Standard deviation22.518464
Coefficient of variation (CV)1.0277138
Kurtosis-1.5010774
Mean21.91122
Median Absolute Deviation (MAD)9.362
Skewness0.49432208
Sum2191.122
Variance507.0812
MonotonicityNot monotonic
2023-12-10T19:49:27.022390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
9.362 3
 
3.0%
52.798 3
 
3.0%
33.738 3
 
3.0%
54.218 2
 
2.0%
33.21 2
 
2.0%
53.254 2
 
2.0%
52.863 2
 
2.0%
2.201 2
 
2.0%
2.225 2
 
2.0%
Other values (60) 65
65.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.8 1
 
1.0%
0.805 2
 
2.0%
0.858 1
 
1.0%
0.885 1
 
1.0%
0.968 1
 
1.0%
1.025 1
 
1.0%
1.029 1
 
1.0%
1.073 1
 
1.0%
1.098 1
 
1.0%
ValueCountFrequency (%)
58.662 1
1.0%
58.462 1
1.0%
56.234 1
1.0%
56.128 1
1.0%
55.809 1
1.0%
55.597 1
1.0%
55.067 1
1.0%
54.642 1
1.0%
54.218 2
2.0%
54.006 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.399
Minimum0
Maximum118.7
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:27.283468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median94.6
Q3100.1
95-th percentile107.3
Maximum118.7
Range118.7
Interquartile range (IQR)99.9

Descriptive statistics

Standard deviation46.399868
Coefficient of variation (CV)0.76822246
Kurtosis-1.794789
Mean60.399
Median Absolute Deviation (MAD)10.3
Skewness-0.36784307
Sum6039.9
Variance2152.9478
MonotonicityNot monotonic
2023-12-10T19:49:27.548809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.1 8
 
8.0%
0.2 5
 
5.0%
100.1 3
 
3.0%
101.0 3
 
3.0%
95.8 3
 
3.0%
107.3 3
 
3.0%
14.3 3
 
3.0%
97.3 3
 
3.0%
97.5 2
 
2.0%
Other values (44) 53
53.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.1 8
8.0%
0.2 5
 
5.0%
13.2 2
 
2.0%
13.4 1
 
1.0%
14.0 1
 
1.0%
14.1 2
 
2.0%
14.2 2
 
2.0%
14.3 3
 
3.0%
14.4 1
 
1.0%
ValueCountFrequency (%)
118.7 1
 
1.0%
110.4 1
 
1.0%
108.3 1
 
1.0%
107.3 3
3.0%
106.7 1
 
1.0%
103.1 1
 
1.0%
102.6 1
 
1.0%
102.4 1
 
1.0%
102.2 1
 
1.0%
101.6 2
2.0%

Interactions

2023-12-10T19:49:21.732643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:15.141358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:16.118666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:17.180467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:18.248305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:19.494903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:20.507362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:21.876493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:15.286690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:16.246963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:17.339269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:18.386031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:19.610022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:20.690033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:22.024993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:15.448760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:16.395013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:17.487603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:18.530075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:19.755000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:20.879941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:22.182765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:15.573196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:16.545048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:17.661689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:18.946689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:19.912923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:21.094483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:22.339699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:15.680306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:16.704495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:17.811399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:19.084197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:20.049426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:21.253863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:22.497173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:15.790104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:16.863216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:17.952801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:19.236862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:20.209081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:21.411185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:22.671726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:15.935349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:17.020259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:18.108417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:19.378463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:20.349231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:21.569932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:49:27.728683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0000.9560.3140.5540.5980.9220.906
일자/시간(t)0.0001.0000.0000.4660.5730.5740.0000.000
저수위(m)0.9560.0001.0000.0000.5170.5480.9250.748
강우량(mm)0.3140.4660.0001.0000.5030.4980.2600.308
유입량(ms)0.5540.5730.5170.5031.0000.9950.5040.791
방류량(ms)0.5980.5740.5480.4980.9951.0000.5160.856
저수량(백만m3)0.9220.0000.9250.2600.5040.5161.0000.831
저수율0.9060.0000.7480.3080.7910.8560.8311.000
2023-12-10T19:49:27.935461image/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)1.0000.011-0.145-0.289-0.294-0.025-0.1100.000
저수위(m)0.0111.000-0.1930.0410.030-0.0970.1060.881
강우량(mm)-0.145-0.1931.0000.2600.2640.2490.1590.061
유입량(ms)-0.2890.0410.2601.0000.9980.7330.7550.334
방류량(ms)-0.2940.0300.2640.9981.0000.7330.7510.371
저수량(백만m3)-0.025-0.0970.2490.7330.7331.0000.7080.810
저수율-0.1100.1060.1590.7550.7510.7081.0000.768
댐이름0.0000.8810.0610.3340.3710.8100.7681.000

Missing values

2023-12-10T19:49:22.889190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:49:23.096139image/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
0강천보2021042738.050.073.61472.0428.954102.6
1강천보2021040438.365.9613209.438193.72310.357118.7
2강천보2021041238.1610.8213147.451145.8799.452108.3
3강천보2021041038.140.0143.958142.919.362107.3
4강천보2021040838.140.0149.028152.1719.362107.3
5강천보2021040238.030.0115.908116.9568.864101.6
6강천보2021042338.00.066.12763.6198.728100.0
7강천보2021041138.130.0141.715142.2399.316106.7
8강천보2021040738.20.0158.014159.0629.633110.4
9강천보2021042638.020.064.2264.228.819101.0
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90달성보2021040813.590.0230.273227.81754.21892.6
91달성보2021040413.7816.5727522.417499.08556.23496.0
92달성보2021041713.770.0147.066147.06656.12895.8
93달성보2021041813.740.0141.142144.82655.80995.3
94달성보2021041913.720.0130.933133.38955.59794.9
95달성보2021040713.570.0143222.087228.22754.00692.2
96백제보202104032.522.411864.61871.97913.3555.2
97백제보202104162.470.261351.56449.1113.19154.6
98백제보202104272.080.028.33527.72211.12446.0
99백제보202104241.980.027.79132.02310.643.9