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
강우량(mm) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 4 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 39 (39.0%) zerosZeros
유입량(ms) has 31 (31.0%) zerosZeros
방류량(ms) has 31 (31.0%) zerosZeros
저수량(백만m3) has 31 (31.0%) zerosZeros
저수율 has 31 (31.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:51:14.644031
Analysis finished2023-12-10 10:51:22.682014
Duration8.04 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
31 
공주보
31 
구담보
31 
구미보
7 

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 31
31.0%
공주보 31
31.0%
구담보 31
31.0%
구미보 7
 
7.0%

Length

2023-12-10T19:51:22.778997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:51:22.942166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 31
31.0%
공주보 31
31.0%
구담보 31
31.0%
구미보 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200815
Minimum20200801
Maximum20200831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:23.140239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200801
5-th percentile20200802
Q120200807
median20200815
Q320200823
95-th percentile20200830
Maximum20200831
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5613535 × 10-7
Kurtosis-1.2602055
Mean20200815
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0200815 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T19:51:23.377966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20200801 4
 
4.0%
20200803 4
 
4.0%
20200804 4
 
4.0%
20200805 4
 
4.0%
20200806 4
 
4.0%
20200807 4
 
4.0%
20200802 4
 
4.0%
20200826 3
 
3.0%
20200822 3
 
3.0%
20200823 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20200801 4
4.0%
20200802 4
4.0%
20200803 4
4.0%
20200804 4
4.0%
20200805 4
4.0%
20200806 4
4.0%
20200807 4
4.0%
20200808 3
3.0%
20200809 3
3.0%
20200810 3
3.0%
ValueCountFrequency (%)
20200831 3
3.0%
20200830 3
3.0%
20200829 3
3.0%
20200828 3
3.0%
20200827 3
3.0%
20200826 3
3.0%
20200825 3
3.0%
20200824 3
3.0%
20200823 3
3.0%
20200822 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.5486
Minimum3.82
Maximum65.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:23.610860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.82
5-th percentile4.198
Q19.6425
median38.285
Q360.4
95-th percentile63.284
Maximum65.01
Range61.19
Interquartile range (IQR)50.7575

Descriptive statistics

Standard deviation22.08924
Coefficient of variation (CV)0.62138142
Kurtosis-1.3672174
Mean35.5486
Median Absolute Deviation (MAD)23.335
Skewness-0.17283812
Sum3554.86
Variance487.93451
MonotonicityNot monotonic
2023-12-10T19:51:23.867446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.4 7
 
7.0%
62.53 5
 
5.0%
38.31 4
 
4.0%
38.28 3
 
3.0%
38.23 2
 
2.0%
4.57 2
 
2.0%
32.61 2
 
2.0%
3.82 2
 
2.0%
38.29 2
 
2.0%
38.18 2
 
2.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
3.82 2
2.0%
3.91 1
1.0%
4.12 1
1.0%
4.16 1
1.0%
4.2 1
1.0%
4.21 1
1.0%
4.25 1
1.0%
4.27 1
1.0%
4.36 1
1.0%
4.51 1
1.0%
ValueCountFrequency (%)
65.01 1
 
1.0%
64.66 1
 
1.0%
64.14 1
 
1.0%
63.97 1
 
1.0%
63.36 1
 
1.0%
63.28 1
 
1.0%
63.12 1
 
1.0%
62.54 1
 
1.0%
62.53 5
5.0%
62.48 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.059027
Minimum0
Maximum90.1668
Zeros39
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:24.139084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.12805
Q318.85725
95-th percentile45.888195
Maximum90.1668
Range90.1668
Interquartile range (IQR)18.85725

Descriptive statistics

Standard deviation17.705255
Coefficient of variation (CV)1.6009777
Kurtosis4.3827291
Mean11.059027
Median Absolute Deviation (MAD)0.12805
Skewness2.0089111
Sum1105.9027
Variance313.47606
MonotonicityNot monotonic
2023-12-10T19:51:24.400374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 39
39.0%
0.0136 2
 
2.0%
11.0464 1
 
1.0%
24.121 1
 
1.0%
19.1639 1
 
1.0%
34.9334 1
 
1.0%
30.3461 1
 
1.0%
22.2752 1
 
1.0%
45.8088 1
 
1.0%
15.6065 1
 
1.0%
Other values (51) 51
51.0%
ValueCountFrequency (%)
0.0 39
39.0%
0.0136 2
 
2.0%
0.0147 1
 
1.0%
0.0315 1
 
1.0%
0.041 1
 
1.0%
0.0665 1
 
1.0%
0.0684 1
 
1.0%
0.0887 1
 
1.0%
0.1058 1
 
1.0%
0.1095 1
 
1.0%
ValueCountFrequency (%)
90.1668 1
1.0%
66.6505 1
1.0%
61.1656 1
1.0%
57.3013 1
1.0%
47.3967 1
1.0%
45.8088 1
1.0%
44.4157 1
1.0%
44.0675 1
1.0%
41.7582 1
1.0%
35.8106 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1155.3813
Minimum0
Maximum5613.054
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:24.843443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median553.1455
Q31916.6227
95-th percentile4307.1128
Maximum5613.054
Range5613.054
Interquartile range (IQR)1916.6227

Descriptive statistics

Standard deviation1493.4358
Coefficient of variation (CV)1.2925914
Kurtosis0.95174909
Mean1155.3813
Median Absolute Deviation (MAD)553.1455
Skewness1.4032867
Sum115538.13
Variance2230350.6
MonotonicityNot monotonic
2023-12-10T19:51:25.033825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
165.038 1
 
1.0%
167.295 1
 
1.0%
251.426 1
 
1.0%
259.643 1
 
1.0%
314.14 1
 
1.0%
355.191 1
 
1.0%
355.609 1
 
1.0%
554.297 1
 
1.0%
902.069 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
85.926 1
 
1.0%
123.252 1
 
1.0%
152.206 1
 
1.0%
165.038 1
 
1.0%
167.295 1
 
1.0%
173.277 1
 
1.0%
187.356 1
 
1.0%
187.551 1
 
1.0%
230.06 1
 
1.0%
ValueCountFrequency (%)
5613.054 1
1.0%
5421.174 1
1.0%
5067.253 1
1.0%
4827.161 1
1.0%
4345.813 1
1.0%
4305.076 1
1.0%
4243.556 1
1.0%
4210.979 1
1.0%
4145.868 1
1.0%
4025.307 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1157.3928
Minimum0
Maximum5528.713
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:25.239855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median566.92
Q31940.6288
95-th percentile4284.966
Maximum5528.713
Range5528.713
Interquartile range (IQR)1940.6288

Descriptive statistics

Standard deviation1494.1991
Coefficient of variation (CV)1.2910043
Kurtosis0.92304793
Mean1157.3928
Median Absolute Deviation (MAD)566.92
Skewness1.3971578
Sum115739.28
Variance2232631
MonotonicityNot monotonic
2023-12-10T19:51:25.426289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
164.537 1
 
1.0%
169.243 1
 
1.0%
251.741 1
 
1.0%
260.037 1
 
1.0%
315.322 1
 
1.0%
356.373 1
 
1.0%
357.264 1
 
1.0%
577.655 1
 
1.0%
900.497 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
85.926 1
 
1.0%
122.7 1
 
1.0%
149.587 1
 
1.0%
164.537 1
 
1.0%
169.243 1
 
1.0%
174.849 1
 
1.0%
185.784 1
 
1.0%
187.551 1
 
1.0%
229.012 1
 
1.0%
ValueCountFrequency (%)
5528.713 1
1.0%
5469.536 1
1.0%
5075.248 1
1.0%
4835.956 1
1.0%
4374.38 1
1.0%
4280.26 1
1.0%
4239.109 1
1.0%
4203.351 1
1.0%
4166.793 1
1.0%
4011.799 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.4212
Minimum0
Maximum54.361
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:25.587419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.045
Q312.3095
95-th percentile53.2605
Maximum54.361
Range54.361
Interquartile range (IQR)12.3095

Descriptive statistics

Standard deviation14.149743
Coefficient of variation (CV)1.3577844
Kurtosis3.700071
Mean10.4212
Median Absolute Deviation (MAD)6.907
Skewness2.0302576
Sum1042.12
Variance200.21523
MonotonicityNot monotonic
2023-12-10T19:51:25.800107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
10.131 4
 
4.0%
9.995 3
 
3.0%
9.543 2
 
2.0%
9.769 2
 
2.0%
2.138 2
 
2.0%
2.613 2
 
2.0%
10.041 2
 
2.0%
53.449 2
 
2.0%
2.47 1
 
1.0%
Other values (49) 49
49.0%
ValueCountFrequency (%)
0.0 31
31.0%
2.138 2
 
2.0%
2.182 1
 
1.0%
2.307 1
 
1.0%
2.334 1
 
1.0%
2.361 1
 
1.0%
2.368 1
 
1.0%
2.395 1
 
1.0%
2.409 1
 
1.0%
2.47 1
 
1.0%
ValueCountFrequency (%)
54.361 1
1.0%
53.514 1
1.0%
53.449 2
2.0%
53.384 1
1.0%
53.254 1
1.0%
53.124 1
1.0%
34.421 1
1.0%
31.188 1
1.0%
28.757 1
1.0%
28.433 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.829
Minimum0
Maximum244.8
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:25.967602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median77.8
Q3114.625
95-th percentile188.635
Maximum244.8
Range244.8
Interquartile range (IQR)114.625

Descriptive statistics

Standard deviation66.745132
Coefficient of variation (CV)0.98402057
Kurtosis-0.75636038
Mean67.829
Median Absolute Deviation (MAD)62.5
Skewness0.54235753
Sum6782.9
Variance4454.9126
MonotonicityNot monotonic
2023-12-10T19:51:26.166415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
116.1 4
 
4.0%
114.5 3
 
3.0%
109.3 2
 
2.0%
115.0 2
 
2.0%
13.8 2
 
2.0%
111.9 2
 
2.0%
15.2 2
 
2.0%
16.8 2
 
2.0%
101.4 2
 
2.0%
Other values (48) 48
48.0%
ValueCountFrequency (%)
0.0 31
31.0%
13.8 2
 
2.0%
14.0 1
 
1.0%
14.8 1
 
1.0%
15.0 1
 
1.0%
15.2 2
 
2.0%
15.4 1
 
1.0%
15.5 1
 
1.0%
15.9 1
 
1.0%
16.6 1
 
1.0%
ValueCountFrequency (%)
244.8 1
1.0%
221.5 1
1.0%
204.8 1
1.0%
200.7 1
1.0%
196.9 1
1.0%
188.2 1
1.0%
185.0 1
1.0%
183.0 1
1.0%
180.3 1
1.0%
161.3 1
1.0%

Interactions

2023-12-10T19:51:21.317122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:15.088352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:16.123942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.141635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:18.113735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:19.315766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:20.309739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:21.474698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:15.256747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:16.277440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.275765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:18.276839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:19.444009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:20.469804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:21.621143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:15.407808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:16.420983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.403264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:18.422536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:19.582520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:20.625778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:21.756479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:15.551136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:16.558704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.524547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:18.553378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:19.723913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:20.772764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:21.892715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:15.703458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:16.704435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.668021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:18.936910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:19.879755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:20.911697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:22.040094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:15.848830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:16.850389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.807202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:19.066273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:20.021272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:21.055967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:22.194111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:15.995967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.006754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:17.948591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:19.197840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:20.171441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:21.172355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:51:26.312857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.3230.6600.6540.9800.722
일자/시간(t)0.0001.0000.3810.4150.4690.5280.4200.266
저수위(m)1.0000.3811.0000.4360.8570.8540.9110.766
강우량(mm)0.3230.4150.4361.0000.7660.7630.6790.927
유입량(ms)0.6600.4690.8570.7661.0001.0000.7880.804
방류량(ms)0.6540.5280.8540.7631.0001.0000.7850.806
저수량(백만m3)0.9800.4200.9110.6790.7880.7851.0000.869
저수율0.7220.2660.7660.9270.8040.8060.8691.000
2023-12-10T19:51:26.491810image/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)1.000-0.103-0.260-0.369-0.367-0.411-0.2140.000
저수위(m)-0.1031.000-0.455-0.501-0.501-0.489-0.3690.995
강우량(mm)-0.260-0.4551.0000.7980.7930.7880.7810.204
유입량(ms)-0.369-0.5010.7981.0001.0000.8820.8790.445
방류량(ms)-0.367-0.5010.7931.0001.0000.8800.8780.440
저수량(백만m3)-0.411-0.4890.7880.8820.8801.0000.8770.795
저수율-0.214-0.3690.7810.8790.8780.8771.0000.545
댐이름0.0000.9950.2040.4450.4400.7950.5451.000

Missing values

2023-12-10T19:51:22.384829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:51:22.601363image/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
0강천보2020080138.1526.4581902.069900.4979.407107.8
1강천보2020080238.4490.16683864.2653849.07410.719122.8
2강천보2020080339.666.65052927.4612844.64417.875204.8
3강천보2020080438.8523.02514145.8684203.35112.908147.9
4강천보2020080539.0521.33744025.3074011.79914.075161.3
5강천보2020080640.1547.39675613.0545528.71321.363244.8
6강천보2020080739.55.56055421.1745469.53617.184196.9
7강천보2020080839.3933.09624827.1614835.95616.424188.2
8강천보2020080939.2930.00285067.2535075.24815.733180.3
9강천보2020081038.1918.81653078.7163149.8439.588109.9
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구담보2020082960.40.00.00.00.00.0
91구담보2020083060.40.00.00.00.00.0
92구담보2020083160.40.00.00.00.00.0
93구미보2020080132.64.3897814.404814.40453.384101.2
94구미보2020080232.7518.07621158.6481147.34254.361103.1
95구미보2020080332.561.98161069.2971083.61753.124100.7
96구미보2020080432.6111.2267737.623733.85453.449101.4
97구미보2020080532.628.2309786.964786.2153.514101.5
98구미보2020080632.6117.5193899.572900.32653.449101.4
99구미보2020080732.5828.89581586.2451588.50653.254101.0