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 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) 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 4 other fieldsHigh correlation
강우량(mm) has 56 (56.0%) zerosZeros
유입량(ms) has 30 (30.0%) zerosZeros
방류량(ms) has 30 (30.0%) zerosZeros
저수량(백만m3) has 30 (30.0%) zerosZeros
저수율 has 30 (30.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:54:53.571912
Analysis finished2023-12-10 10:55:02.424799
Duration8.85 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
30 
공주보
30 
구담보
30 
구미보
10 

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 30
30.0%
공주보 30
30.0%
구담보 30
30.0%
구미보 10
 
10.0%

Length

2023-12-10T19:55:02.521291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:02.676116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 30
30.0%
공주보 30
30.0%
구담보 30
30.0%
구미보 10
 
10.0%

일자/시간(t)
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190414
Minimum20190401
Maximum20190430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:03.028733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190401
5-th percentile20190402
Q120190407
median20190414
Q320190422
95-th percentile20190429
Maximum20190430
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3750842 × 10-7
Kurtosis-1.2329291
Mean20190414
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0190414 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T19:55:03.337216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190401 4
 
4.0%
20190403 4
 
4.0%
20190404 4
 
4.0%
20190405 4
 
4.0%
20190406 4
 
4.0%
20190407 4
 
4.0%
20190408 4
 
4.0%
20190409 4
 
4.0%
20190410 4
 
4.0%
20190402 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20190401 4
4.0%
20190402 4
4.0%
20190403 4
4.0%
20190404 4
4.0%
20190405 4
4.0%
20190406 4
4.0%
20190407 4
4.0%
20190408 4
4.0%
20190409 4
4.0%
20190410 4
4.0%
ValueCountFrequency (%)
20190430 3
3.0%
20190429 3
3.0%
20190428 3
3.0%
20190427 3
3.0%
20190426 3
3.0%
20190425 3
3.0%
20190424 3
3.0%
20190423 3
3.0%
20190422 3
3.0%
20190421 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.1008
Minimum4.24
Maximum67.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:03.568259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.24
5-th percentile4.2795
Q14.3375
median38.225
Q362.51
95-th percentile66.429
Maximum67.68
Range63.44
Interquartile range (IQR)58.1725

Descriptive statistics

Standard deviation23.201204
Coefficient of variation (CV)0.6609879
Kurtosis-1.3383919
Mean35.1008
Median Absolute Deviation (MAD)24.29
Skewness-0.14100965
Sum3510.08
Variance538.29587
MonotonicityNot monotonic
2023-12-10T19:55:03.853010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.32 6
 
6.0%
62.52 6
 
6.0%
62.51 6
 
6.0%
4.31 5
 
5.0%
38.33 5
 
5.0%
4.29 4
 
4.0%
32.51 4
 
4.0%
38.25 4
 
4.0%
4.34 4
 
4.0%
38.22 3
 
3.0%
Other values (42) 53
53.0%
ValueCountFrequency (%)
4.24 1
 
1.0%
4.25 1
 
1.0%
4.26 1
 
1.0%
4.27 2
 
2.0%
4.28 1
 
1.0%
4.29 4
4.0%
4.3 2
 
2.0%
4.31 5
5.0%
4.32 6
6.0%
4.33 2
 
2.0%
ValueCountFrequency (%)
67.68 1
1.0%
67.66 1
1.0%
67.65 1
1.0%
67.58 1
1.0%
67.55 1
1.0%
66.37 1
1.0%
65.38 1
1.0%
62.62 1
1.0%
62.55 2
2.0%
62.54 1
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.596374
Minimum0
Maximum25.2071
Zeros56
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:04.106980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.084775
95-th percentile8.84892
Maximum25.2071
Range25.2071
Interquartile range (IQR)0.084775

Descriptive statistics

Standard deviation4.6791741
Coefficient of variation (CV)2.9311265
Kurtosis14.928728
Mean1.596374
Median Absolute Deviation (MAD)0
Skewness3.7722345
Sum159.6374
Variance21.89467
MonotonicityNot monotonic
2023-12-10T19:55:04.366359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 56
56.0%
0.008 2
 
2.0%
0.0192 1
 
1.0%
2.5915 1
 
1.0%
0.7044 1
 
1.0%
0.0804 1
 
1.0%
0.012 1
 
1.0%
0.0335 1
 
1.0%
0.0201 1
 
1.0%
4.4251 1
 
1.0%
Other values (34) 34
34.0%
ValueCountFrequency (%)
0.0 56
56.0%
0.008 2
 
2.0%
0.0112 1
 
1.0%
0.012 1
 
1.0%
0.0138 1
 
1.0%
0.0161 1
 
1.0%
0.0192 1
 
1.0%
0.0201 1
 
1.0%
0.0234 1
 
1.0%
0.0238 1
 
1.0%
ValueCountFrequency (%)
25.2071 1
1.0%
25.1798 1
1.0%
21.0881 1
1.0%
13.9647 1
1.0%
10.8006 1
1.0%
8.7462 1
1.0%
8.7231 1
1.0%
8.4085 1
1.0%
7.5907 1
1.0%
6.0391 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.05084
Minimum0
Maximum293.726
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:04.608747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.71
Q3167.32375
95-th percentile234.84075
Maximum293.726
Range293.726
Interquartile range (IQR)167.32375

Descriptive statistics

Standard deviation86.857608
Coefficient of variation (CV)1.1272766
Kurtosis-0.61017129
Mean77.05084
Median Absolute Deviation (MAD)37.71
Skewness0.93573808
Sum7705.084
Variance7544.244
MonotonicityNot monotonic
2023-12-10T19:55:04.853846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
167.273 1
 
1.0%
53.708 1
 
1.0%
38.993 1
 
1.0%
40.216 1
 
1.0%
38.631 1
 
1.0%
37.607 1
 
1.0%
38.63 1
 
1.0%
43.785 1
 
1.0%
37.603 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
23.766 1
 
1.0%
30.939 1
 
1.0%
31.15 1
 
1.0%
31.423 1
 
1.0%
31.425 1
 
1.0%
32.225 1
 
1.0%
32.675 1
 
1.0%
32.732 1
 
1.0%
33.495 1
 
1.0%
ValueCountFrequency (%)
293.726 1
1.0%
288.165 1
1.0%
260.494 1
1.0%
237.562 1
1.0%
235.254 1
1.0%
234.819 1
1.0%
233.345 1
1.0%
231.278 1
1.0%
222.733 1
1.0%
222.179 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.87436
Minimum0
Maximum288.165
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:05.161002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38.025
Q3166.6635
95-th percentile234.6198
Maximum288.165
Range288.165
Interquartile range (IQR)166.6635

Descriptive statistics

Standard deviation86.748987
Coefficient of variation (CV)1.1284515
Kurtosis-0.61226103
Mean76.87436
Median Absolute Deviation (MAD)38.025
Skewness0.93908066
Sum7687.436
Variance7525.3867
MonotonicityNot monotonic
2023-12-10T19:55:05.767958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
154.177 1
 
1.0%
53.077 1
 
1.0%
39.072 1
 
1.0%
40.216 1
 
1.0%
38.237 1
 
1.0%
38.238 1
 
1.0%
38.157 1
 
1.0%
43.706 1
 
1.0%
37.997 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
22.077 1
 
1.0%
30.396 1
 
1.0%
31.018 1
 
1.0%
31.502 1
 
1.0%
32.179 1
 
1.0%
32.383 1
 
1.0%
32.596 1
 
1.0%
32.653 1
 
1.0%
33.495 1
 
1.0%
ValueCountFrequency (%)
288.165 1
1.0%
281.154 1
1.0%
272.019 1
1.0%
241.203 1
1.0%
238.397 1
1.0%
234.421 1
1.0%
233.895 1
1.0%
231.676 1
1.0%
223.227 1
1.0%
220.114 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.99291
Minimum0
Maximum53.384
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:06.037771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.443
Q39.86
95-th percentile52.80125
Maximum53.384
Range53.384
Interquartile range (IQR)9.86

Descriptive statistics

Standard deviation15.259929
Coefficient of variation (CV)1.6968845
Kurtosis4.335749
Mean8.99291
Median Absolute Deviation (MAD)2.443
Skewness2.373203
Sum899.291
Variance232.86544
MonotonicityNot monotonic
2023-12-10T19:55:06.390200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 30
30.0%
2.443 6
 
6.0%
2.436 5
 
5.0%
10.222 5
 
5.0%
2.457 4
 
4.0%
2.422 4
 
4.0%
52.798 4
 
4.0%
9.86 4
 
4.0%
9.724 3
 
3.0%
2.409 2
 
2.0%
Other values (27) 33
33.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.388 1
 
1.0%
2.395 1
 
1.0%
2.402 1
 
1.0%
2.409 2
 
2.0%
2.416 1
 
1.0%
2.422 4
 
4.0%
2.429 2
 
2.0%
2.436 5
 
5.0%
2.443 6
 
6.0%
ValueCountFrequency (%)
53.384 1
 
1.0%
53.254 1
 
1.0%
53.124 1
 
1.0%
52.863 2
2.0%
52.798 4
4.0%
52.652 1
 
1.0%
10.538 1
 
1.0%
10.493 1
 
1.0%
10.403 1
 
1.0%
10.267 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.76
Minimum0
Maximum120.7
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:06.637260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.7
Q3109.7
95-th percentile117.1
Maximum120.7
Range120.7
Interquartile range (IQR)109.7

Descriptive statistics

Standard deviation50.926876
Coefficient of variation (CV)1.0444396
Kurtosis-1.7950391
Mean48.76
Median Absolute Deviation (MAD)15.7
Skewness0.39149974
Sum4876
Variance2593.5467
MonotonicityNot monotonic
2023-12-10T19:55:06.879436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 30
30.0%
15.7 11
 
11.0%
15.6 6
 
6.0%
15.8 6
 
6.0%
117.1 5
 
5.0%
100.1 4
 
4.0%
113.0 4
 
4.0%
15.5 4
 
4.0%
111.4 3
 
3.0%
114.5 2
 
2.0%
Other values (21) 25
25.0%
ValueCountFrequency (%)
0.0 30
30.0%
15.4 2
 
2.0%
15.5 4
 
4.0%
15.6 6
 
6.0%
15.7 11
 
11.0%
15.8 6
 
6.0%
15.9 1
 
1.0%
99.9 1
 
1.0%
100.1 4
 
4.0%
100.3 2
 
2.0%
ValueCountFrequency (%)
120.7 1
 
1.0%
120.2 1
 
1.0%
119.2 1
 
1.0%
117.6 1
 
1.0%
117.1 5
5.0%
116.1 1
 
1.0%
115.0 1
 
1.0%
114.5 2
 
2.0%
114.0 1
 
1.0%
113.0 4
4.0%

Interactions

2023-12-10T19:55:00.937827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:54.008015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:55.227394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:56.340210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:57.384315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:58.624248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.042209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.104015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:54.184407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:55.391680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:56.493276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:57.544921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:58.770891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.201522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.245645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:54.341343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:55.570163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:56.641393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:57.716721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:58.915746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.325212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.371386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:54.497236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:55.719668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:56.777097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:57.901922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:59.057517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.441351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.499920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:54.693077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:55.873702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:56.922568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:58.065853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:59.177393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.565334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.628124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:54.868114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:56.033646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:57.081692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:58.244142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:59.588915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.681533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:01.789365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:55.054347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:56.187520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:57.226000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:58.448488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:59.868673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:00.804489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:55:07.048842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.4220.9800.9801.0000.998
일자/시간(t)0.0001.0000.0000.3380.4320.4480.0000.000
저수위(m)1.0000.0001.0000.4220.9800.9801.0000.998
강우량(mm)0.4220.3380.4221.0000.7850.7680.3170.487
유입량(ms)0.9800.4320.9800.7851.0000.9940.8090.976
방류량(ms)0.9800.4480.9800.7680.9941.0000.8090.979
저수량(백만m3)1.0000.0001.0000.3170.8090.8091.0000.846
저수율0.9980.0000.9980.4870.9760.9790.8461.000
2023-12-10T19:55:07.262082image/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)1.000-0.0090.2210.0620.060-0.215-0.0900.000
저수위(m)-0.0091.000-0.433-0.359-0.364-0.365-0.2941.000
강우량(mm)0.221-0.4331.0000.5180.5090.4150.4520.193
유입량(ms)0.062-0.3590.5181.0000.9970.7960.9330.791
방류량(ms)0.060-0.3640.5090.9971.0000.7910.9280.791
저수량(백만m3)-0.215-0.3650.4150.7960.7911.0000.9250.995
저수율-0.090-0.2940.4520.9330.9280.9251.0000.946
댐이름0.0001.0000.1930.7910.7910.9950.9461.000

Missing values

2023-12-10T19:55:02.021954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:55:02.340335image/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
0강천보2019040138.290.0192167.273154.17710.041115.0
1강천보2019040238.340.0238222.733220.11410.267117.6
2강천보2019040338.280.0231.278234.4219.995114.5
3강천보2019040438.370.2409221.461216.74610.403119.2
4강천보2019040538.330.0209.729211.82410.222117.1
5강천보2019040638.330.5828202.452202.45210.222117.1
6강천보2019040738.330.0461208.653208.65310.222117.1
7강천보2019040838.310.0222.179223.22710.131116.1
8강천보2019040938.0921.0881260.494272.0199.135104.7
9강천보2019041038.095.9549288.165288.1659.135104.7
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2019040132.580.064.77258.74253.254101.0
91구미보2019040232.490.034.8241.78552.65299.9
92구미보2019040332.510.023423.76622.07752.798100.1
93구미보2019040432.510.037.65937.65952.798100.1
94구미보2019040532.520.013831.1530.39652.863100.3
95구미보2019040632.520.041.71641.71652.863100.3
96구미보2019040732.510.038131.42532.17952.798100.1
97구미보2019040832.510.034.79234.79252.798100.1
98구미보2019040932.5625.207139.63835.86953.124100.7
99구미보2019041032.610.800685.97882.96353.384101.2