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 57 (57.0%) 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 10:54:24.058558
Analysis finished2023-12-10 10:54:32.698243
Duration8.64 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

2023-12-10T19:54:32.825680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:33.028285image/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%
Mean20190614
Minimum20190601
Maximum20190630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:33.225843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190601
5-th percentile20190602
Q120190607
median20190614
Q320190622
95-th percentile20190629
Maximum20190630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3750408 × 10-7
Kurtosis-1.2329291
Mean20190614
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0190614 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T19:54:33.458365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190601 4
 
4.0%
20190603 4
 
4.0%
20190604 4
 
4.0%
20190605 4
 
4.0%
20190606 4
 
4.0%
20190607 4
 
4.0%
20190608 4
 
4.0%
20190609 4
 
4.0%
20190610 4
 
4.0%
20190602 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20190601 4
4.0%
20190602 4
4.0%
20190603 4
4.0%
20190604 4
4.0%
20190605 4
4.0%
20190606 4
4.0%
20190607 4
4.0%
20190608 4
4.0%
20190609 4
4.0%
20190610 4
4.0%
ValueCountFrequency (%)
20190630 3
3.0%
20190629 3
3.0%
20190628 3
3.0%
20190627 3
3.0%
20190626 3
3.0%
20190625 3
3.0%
20190624 3
3.0%
20190623 3
3.0%
20190622 3
3.0%
20190621 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.7287
Minimum4.27
Maximum62.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:33.671029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.27
5-th percentile4.3
Q14.3375
median38.015
Q362.55
95-th percentile62.5805
Maximum62.64
Range58.37
Interquartile range (IQR)58.2125

Descriptive statistics

Standard deviation22.773798
Coefficient of variation (CV)0.65576304
Kurtosis-1.3519403
Mean34.7287
Median Absolute Deviation (MAD)24.545
Skewness-0.16995048
Sum3472.87
Variance518.64587
MonotonicityNot monotonic
2023-12-10T19:54:33.874375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
38.02 10
 
10.0%
38.01 10
 
10.0%
62.56 9
 
9.0%
38.03 8
 
8.0%
4.31 7
 
7.0%
4.32 6
 
6.0%
62.55 6
 
6.0%
4.3 5
 
5.0%
62.57 4
 
4.0%
32.53 3
 
3.0%
Other values (21) 32
32.0%
ValueCountFrequency (%)
4.27 1
 
1.0%
4.28 1
 
1.0%
4.29 2
 
2.0%
4.3 5
5.0%
4.31 7
7.0%
4.32 6
6.0%
4.33 3
3.0%
4.34 2
 
2.0%
4.4 1
 
1.0%
4.45 1
 
1.0%
ValueCountFrequency (%)
62.64 1
 
1.0%
62.6 1
 
1.0%
62.59 3
 
3.0%
62.58 3
 
3.0%
62.57 4
4.0%
62.56 9
9.0%
62.55 6
6.0%
62.54 2
 
2.0%
62.52 1
 
1.0%
38.13 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.993454
Minimum0
Maximum31.1678
Zeros57
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:34.060745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.260175
95-th percentile13.51377
Maximum31.1678
Range31.1678
Interquartile range (IQR)0.260175

Descriptive statistics

Standard deviation5.784662
Coefficient of variation (CV)2.9018287
Kurtosis13.304022
Mean1.993454
Median Absolute Deviation (MAD)0
Skewness3.5809438
Sum199.3454
Variance33.462315
MonotonicityNot monotonic
2023-12-10T19:54:34.266036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 57
57.0%
0.0104 2
 
2.0%
1.8271 1
 
1.0%
12.0247 1
 
1.0%
12.6186 1
 
1.0%
0.0654 1
 
1.0%
0.576 1
 
1.0%
0.1015 1
 
1.0%
3.0242 1
 
1.0%
0.0717 1
 
1.0%
Other values (33) 33
33.0%
ValueCountFrequency (%)
0.0 57
57.0%
0.0093 1
 
1.0%
0.0104 2
 
2.0%
0.0119 1
 
1.0%
0.0128 1
 
1.0%
0.0149 1
 
1.0%
0.0166 1
 
1.0%
0.0318 1
 
1.0%
0.0323 1
 
1.0%
0.0361 1
 
1.0%
ValueCountFrequency (%)
31.1678 1
1.0%
30.5017 1
1.0%
22.7846 1
1.0%
17.1188 1
1.0%
15.9585 1
1.0%
13.3851 1
1.0%
12.6186 1
1.0%
12.0247 1
1.0%
10.2818 1
1.0%
9.2879 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.31853
Minimum0
Maximum224.948
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:34.489657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median51.5225
Q380.183
95-th percentile123.9415
Maximum224.948
Range224.948
Interquartile range (IQR)80.183

Descriptive statistics

Standard deviation43.528509
Coefficient of variation (CV)0.8319903
Kurtosis1.4654941
Mean52.31853
Median Absolute Deviation (MAD)29.6735
Skewness0.72425009
Sum5231.853
Variance1894.7311
MonotonicityNot monotonic
2023-12-10T19:54:34.685275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
78.218 1
 
1.0%
48.579 1
 
1.0%
47.35 1
 
1.0%
48.594 1
 
1.0%
48.099 1
 
1.0%
47.337 1
 
1.0%
50.884 1
 
1.0%
52.481 1
 
1.0%
52.516 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
31.291 1
 
1.0%
31.62 1
 
1.0%
44.619 1
 
1.0%
44.671 1
 
1.0%
45.77 1
 
1.0%
45.92 1
 
1.0%
47.229 1
 
1.0%
47.337 1
 
1.0%
47.35 1
 
1.0%
ValueCountFrequency (%)
224.948 1
1.0%
170.098 1
1.0%
143.315 1
1.0%
130.454 1
1.0%
125.547 1
1.0%
123.857 1
1.0%
121.593 1
1.0%
100.763 1
1.0%
99.007 1
1.0%
98.489 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.369
Minimum0
Maximum227.963
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:34.892923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median51.7215
Q380.0655
95-th percentile122.7863
Maximum227.963
Range227.963
Interquartile range (IQR)80.0655

Descriptive statistics

Standard deviation43.61691
Coefficient of variation (CV)0.83287652
Kurtosis1.6324556
Mean52.369
Median Absolute Deviation (MAD)29.604
Skewness0.75324844
Sum5236.9
Variance1902.4349
MonotonicityNot monotonic
2023-12-10T19:54:35.106486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
78.218 1
 
1.0%
48.579 1
 
1.0%
47.508 1
 
1.0%
48.515 1
 
1.0%
47.941 1
 
1.0%
47.416 1
 
1.0%
51.042 1
 
1.0%
52.402 1
 
1.0%
52.516 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
32.045 1
 
1.0%
33.881 1
 
1.0%
44.592 1
 
1.0%
44.934 1
 
1.0%
45.691 1
 
1.0%
46.156 1
 
1.0%
47.229 1
 
1.0%
47.416 1
 
1.0%
47.508 1
 
1.0%
ValueCountFrequency (%)
227.963 1
1.0%
171.438 1
1.0%
140.556 1
1.0%
136.74 1
1.0%
125.547 1
1.0%
122.641 1
1.0%
117.571 1
1.0%
99.715 1
1.0%
97.5 1
1.0%
96.394 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.67978
Minimum0
Maximum53.189
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:35.295867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.443
Q38.819
95-th percentile52.928
Maximum53.189
Range53.189
Interquartile range (IQR)8.819

Descriptive statistics

Standard deviation15.247788
Coefficient of variation (CV)1.7567022
Kurtosis4.5703007
Mean8.67978
Median Absolute Deviation (MAD)2.443
Skewness2.4430676
Sum867.978
Variance232.49505
MonotonicityNot monotonic
2023-12-10T19:54:35.548229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 30
30.0%
8.819 10
 
10.0%
8.773 10
 
10.0%
8.864 8
 
8.0%
2.436 7
 
7.0%
2.443 6
 
6.0%
2.429 5
 
5.0%
52.928 3
 
3.0%
2.45 3
 
3.0%
53.059 2
 
2.0%
Other values (13) 16
16.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.409 1
 
1.0%
2.416 1
 
1.0%
2.422 2
 
2.0%
2.429 5
 
5.0%
2.436 7
 
7.0%
2.443 6
 
6.0%
2.45 3
 
3.0%
2.457 2
 
2.0%
2.497 1
 
1.0%
ValueCountFrequency (%)
53.189 1
 
1.0%
53.059 2
 
2.0%
52.993 1
 
1.0%
52.928 3
 
3.0%
52.798 2
 
2.0%
52.733 1
 
1.0%
9.316 1
 
1.0%
8.954 1
 
1.0%
8.864 8
8.0%
8.819 10
10.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.138
Minimum0
Maximum106.7
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:35.749627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.7
Q3100.5
95-th percentile101.6
Maximum106.7
Range106.7
Interquartile range (IQR)100.5

Descriptive statistics

Standard deviation46.276889
Coefficient of variation (CV)1.0252313
Kurtosis-1.8339773
Mean45.138
Median Absolute Deviation (MAD)15.7
Skewness0.3609655
Sum4513.8
Variance2141.5505
MonotonicityNot monotonic
2023-12-10T19:54:35.937611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.0 30
30.0%
15.7 13
13.0%
100.5 11
 
11.0%
101.0 10
 
10.0%
101.6 8
 
8.0%
15.6 7
 
7.0%
15.8 5
 
5.0%
100.4 3
 
3.0%
100.6 2
 
2.0%
15.5 2
 
2.0%
Other values (8) 9
 
9.0%
ValueCountFrequency (%)
0.0 30
30.0%
15.5 2
 
2.0%
15.6 7
 
7.0%
15.7 13
13.0%
15.8 5
 
5.0%
16.1 1
 
1.0%
16.3 1
 
1.0%
17.0 1
 
1.0%
100.0 1
 
1.0%
100.1 2
 
2.0%
ValueCountFrequency (%)
106.7 1
 
1.0%
102.6 1
 
1.0%
101.6 8
8.0%
101.0 10
10.0%
100.9 1
 
1.0%
100.6 2
 
2.0%
100.5 11
11.0%
100.4 3
 
3.0%
100.1 2
 
2.0%
100.0 1
 
1.0%

Interactions

2023-12-10T19:54:31.054862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:24.453306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:25.781252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:26.712456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:27.789103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:28.886374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:29.975945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:31.212790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:24.621895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:25.935554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:26.852061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:27.956064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:29.057847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:30.142643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:31.626591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:24.807004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:26.093044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:27.000010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:28.119004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:29.209015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:30.294821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:31.784494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:24.966929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:26.226557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:27.138765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:28.266520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:29.367313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:30.451148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:31.924198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:25.124602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:26.341702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:27.266680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:28.408066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:29.511231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:30.601560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:32.065288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:25.459167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:26.474430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:27.403312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:28.558286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:29.648430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:30.743041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:32.207290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:25.629063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:26.607352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:27.548931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:28.725509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:29.808793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:30.908975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:54:36.081855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.2730.8800.8801.0001.000
일자/시간(t)0.0001.0000.0000.3010.0000.0000.0000.000
저수위(m)1.0000.0001.0000.2730.8800.8801.0001.000
강우량(mm)0.2730.3010.2731.0000.7310.7310.2710.000
유입량(ms)0.8800.0000.8800.7311.0001.0000.9280.996
방류량(ms)0.8800.0000.8800.7311.0001.0000.9280.996
저수량(백만m3)1.0000.0001.0000.2710.9280.9281.0000.940
저수율1.0000.0001.0000.0000.9960.9960.9401.000
2023-12-10T19:54:36.643386image/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)1.0000.0640.167-0.088-0.084-0.199-0.0920.000
저수위(m)0.0641.000-0.380-0.396-0.399-0.369-0.3011.000
강우량(mm)0.167-0.3801.0000.5670.5660.4470.4760.120
유입량(ms)-0.088-0.3960.5671.0000.9990.8510.8890.766
방류량(ms)-0.084-0.3990.5660.9991.0000.8460.8840.766
저수량(백만m3)-0.199-0.3690.4470.8510.8461.0000.9390.995
저수율-0.092-0.3010.4760.8890.8840.9391.0000.995
댐이름0.0001.0000.1200.7660.7660.9950.9951.000

Missing values

2023-12-10T19:54:32.379878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:54:32.605931image/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
0강천보2019060138.020.078.21878.2188.819101.0
1강천보2019060238.030.077.83777.3138.864101.6
2강천보2019060338.020.077.78778.3118.819101.0
3강천보2019060438.030.011977.11176.5878.864101.6
4강천보2019060538.020.010475.80176.3258.819101.0
5강천보2019060638.0113.385180.22280.7468.773100.5
6강천보2019060738.1330.5017123.857117.5719.316106.7
7강천보2019060838.010.0361130.454136.748.773100.5
8강천보2019060938.0310.2818100.76399.7158.864101.6
9강천보2019061038.011.3615121.593122.6418.773100.5
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2019060132.540.031.6233.88152.993100.5
91구미보2019060232.530.016631.29132.04552.928100.4
92구미보2019060332.50.051.67353.93452.733100.0
93구미보2019060432.510.078.69577.94152.798100.1
94구미보2019060532.550.059.74856.73353.059100.6
95구미보2019060632.5715.958580.84679.33953.189100.9
96구미보2019060732.5322.7846224.948227.96352.928100.4
97구미보2019060832.530.0562125.547125.54752.928100.4
98구미보2019060932.550.099.00797.553.059100.6
99구미보2019061032.510.448374.4377.44552.798100.1