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 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 3 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 59 (59.0%) 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 10:47:43.430445
Analysis finished2023-12-10 10:47:50.691475
Duration7.26 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

2023-12-10T19:47:50.789030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:47:50.932506image/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%
Mean20211114
Minimum20211101
Maximum20211130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:51.075752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20211101
5-th percentile20211102
Q120211107
median20211114
Q320211122
95-th percentile20211129
Maximum20211130
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3706032 × 10-7
Kurtosis-1.2329291
Mean20211114
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0211114 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T19:47:51.266122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20211101 4
 
4.0%
20211103 4
 
4.0%
20211104 4
 
4.0%
20211105 4
 
4.0%
20211106 4
 
4.0%
20211107 4
 
4.0%
20211108 4
 
4.0%
20211109 4
 
4.0%
20211110 4
 
4.0%
20211102 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20211101 4
4.0%
20211102 4
4.0%
20211103 4
4.0%
20211104 4
4.0%
20211105 4
4.0%
20211106 4
4.0%
20211107 4
4.0%
20211108 4
4.0%
20211109 4
4.0%
20211110 4
4.0%
ValueCountFrequency (%)
20211130 3
3.0%
20211129 3
3.0%
20211128 3
3.0%
20211127 3
3.0%
20211126 3
3.0%
20211125 3
3.0%
20211124 3
3.0%
20211123 3
3.0%
20211122 3
3.0%
20211121 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.8003
Minimum3.65
Maximum60.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:51.435143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.65
5-th percentile3.65
Q13.695
median38.02
Q360.4
95-th percentile60.4
Maximum60.4
Range56.75
Interquartile range (IQR)56.705

Descriptive statistics

Standard deviation22.25987
Coefficient of variation (CV)0.65857018
Kurtosis-1.3554408
Mean33.8003
Median Absolute Deviation (MAD)22.38
Skewness-0.23718729
Sum3380.03
Variance495.5018
MonotonicityNot monotonic
2023-12-10T19:47:51.610136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
60.4 30
30.0%
3.66 15
15.0%
38.09 8
 
8.0%
3.65 6
 
6.0%
38.03 4
 
4.0%
38.02 4
 
4.0%
3.67 3
 
3.0%
38.01 3
 
3.0%
32.59 2
 
2.0%
3.71 2
 
2.0%
Other values (20) 23
23.0%
ValueCountFrequency (%)
3.65 6
 
6.0%
3.66 15
15.0%
3.67 3
 
3.0%
3.68 1
 
1.0%
3.7 1
 
1.0%
3.71 2
 
2.0%
3.73 1
 
1.0%
3.77 1
 
1.0%
30.51 1
 
1.0%
30.54 1
 
1.0%
ValueCountFrequency (%)
60.4 30
30.0%
38.09 8
 
8.0%
38.08 1
 
1.0%
38.07 1
 
1.0%
38.06 2
 
2.0%
38.05 1
 
1.0%
38.04 2
 
2.0%
38.03 4
 
4.0%
38.02 4
 
4.0%
38.01 3
 
3.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.297411
Minimum0
Maximum24.2011
Zeros59
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:51.799132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.066775
95-th percentile6.98786
Maximum24.2011
Range24.2011
Interquartile range (IQR)0.066775

Descriptive statistics

Standard deviation4.5490353
Coefficient of variation (CV)3.5062407
Kurtosis15.749951
Mean1.297411
Median Absolute Deviation (MAD)0
Skewness4.059207
Sum129.7411
Variance20.693722
MonotonicityNot monotonic
2023-12-10T19:47:51.998818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 59
59.0%
0.0482 2
 
2.0%
0.0115 1
 
1.0%
0.2685 1
 
1.0%
0.0059 1
 
1.0%
0.0241 1
 
1.0%
0.0932 1
 
1.0%
4.6436 1
 
1.0%
0.697 1
 
1.0%
0.1539 1
 
1.0%
Other values (31) 31
31.0%
ValueCountFrequency (%)
0.0 59
59.0%
0.0017 1
 
1.0%
0.0059 1
 
1.0%
0.0115 1
 
1.0%
0.0119 1
 
1.0%
0.0125 1
 
1.0%
0.0136 1
 
1.0%
0.0241 1
 
1.0%
0.0344 1
 
1.0%
0.0369 1
 
1.0%
ValueCountFrequency (%)
24.2011 1
1.0%
22.612 1
1.0%
20.5841 1
1.0%
17.3018 1
1.0%
17.2775 1
1.0%
6.4463 1
1.0%
4.6436 1
1.0%
3.6713 1
1.0%
3.1272 1
1.0%
2.6454 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.27064
Minimum0
Maximum123.648
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:52.188802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median42.873
Q392.11
95-th percentile104.9614
Maximum123.648
Range123.648
Interquartile range (IQR)92.11

Descriptive statistics

Standard deviation40.397445
Coefficient of variation (CV)0.80359917
Kurtosis-1.3872024
Mean50.27064
Median Absolute Deviation (MAD)42.873
Skewness0.077274369
Sum5027.064
Variance1631.9535
MonotonicityNot monotonic
2023-12-10T19:47:52.413683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
108.788 1
 
1.0%
42.161 1
 
1.0%
43.517 1
 
1.0%
42.812 1
 
1.0%
42.436 1
 
1.0%
42.221 1
 
1.0%
41.688 1
 
1.0%
41.227 1
 
1.0%
41.883 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
41.029 1
 
1.0%
41.089 1
 
1.0%
41.173 1
 
1.0%
41.227 1
 
1.0%
41.403 1
 
1.0%
41.688 1
 
1.0%
41.883 1
 
1.0%
42.004 1
 
1.0%
42.158 1
 
1.0%
ValueCountFrequency (%)
123.648 1
1.0%
119.814 1
1.0%
114.567 1
1.0%
111.1 1
1.0%
108.788 1
1.0%
104.76 1
1.0%
103.923 1
1.0%
103.626 1
1.0%
103.529 1
1.0%
102.865 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.07797
Minimum0
Maximum138.061
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:52.587488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median42.873
Q392.6835
95-th percentile113.6378
Maximum138.061
Range138.061
Interquartile range (IQR)92.6835

Descriptive statistics

Standard deviation42.508507
Coefficient of variation (CV)0.81624739
Kurtosis-1.3027381
Mean52.07797
Median Absolute Deviation (MAD)42.873
Skewness0.14512044
Sum5207.797
Variance1806.9732
MonotonicityNot monotonic
2023-12-10T19:47:52.781116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
107.74 1
 
1.0%
42.161 1
 
1.0%
43.461 1
 
1.0%
42.812 1
 
1.0%
42.436 1
 
1.0%
42.221 1
 
1.0%
41.632 1
 
1.0%
41.283 1
 
1.0%
41.883 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
41.029 1
 
1.0%
41.145 1
 
1.0%
41.173 1
 
1.0%
41.283 1
 
1.0%
41.403 1
 
1.0%
41.632 1
 
1.0%
41.883 1
 
1.0%
42.06 1
 
1.0%
42.158 1
 
1.0%
ValueCountFrequency (%)
138.061 1
1.0%
133.806 1
1.0%
127.315 1
1.0%
120.089 1
1.0%
115.895 1
1.0%
113.519 1
1.0%
112.148 1
1.0%
107.74 1
1.0%
106.335 1
1.0%
105.293 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9513
Minimum0
Maximum53.71
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:52.963856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.061
Q38.87525
95-th percentile45.611
Maximum53.71
Range53.71
Interquartile range (IQR)8.87525

Descriptive statistics

Standard deviation13.577638
Coefficient of variation (CV)1.7075998
Kurtosis5.0811555
Mean7.9513
Median Absolute Deviation (MAD)2.061
Skewness2.4681367
Sum795.13
Variance184.35226
MonotonicityNot monotonic
2023-12-10T19:47:53.132526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 30
30.0%
2.061 15
15.0%
9.135 8
 
8.0%
2.057 6
 
6.0%
8.864 4
 
4.0%
8.819 4
 
4.0%
2.066 3
 
3.0%
8.773 3
 
3.0%
53.319 2
 
2.0%
2.086 2
 
2.0%
Other values (20) 23
23.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.057 6
 
6.0%
2.061 15
15.0%
2.066 3
 
3.0%
2.071 1
 
1.0%
2.081 1
 
1.0%
2.086 2
 
2.0%
2.095 1
 
1.0%
2.114 1
 
1.0%
8.666 1
 
1.0%
ValueCountFrequency (%)
53.71 1
 
1.0%
53.319 2
 
2.0%
53.254 1
 
1.0%
48.48 1
 
1.0%
45.46 1
 
1.0%
41.949 1
 
1.0%
38.891 1
 
1.0%
38.553 1
 
1.0%
38.35 1
 
1.0%
9.135 8
8.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.509
Minimum0
Maximum104.7
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:47:53.296999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.3
Q3101
95-th percentile104.7
Maximum104.7
Range104.7
Interquartile range (IQR)101

Descriptive statistics

Standard deviation45.995301
Coefficient of variation (CV)1.0571445
Kurtosis-1.7729748
Mean43.509
Median Absolute Deviation (MAD)13.3
Skewness0.41383594
Sum4350.9
Variance2115.5677
MonotonicityNot monotonic
2023-12-10T19:47:53.485099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 30
30.0%
13.3 19
19.0%
104.7 8
 
8.0%
13.2 6
 
6.0%
101.0 5
 
5.0%
101.6 4
 
4.0%
13.4 3
 
3.0%
100.5 3
 
3.0%
101.1 2
 
2.0%
103.1 2
 
2.0%
Other values (16) 18
18.0%
ValueCountFrequency (%)
0.0 30
30.0%
13.2 6
 
6.0%
13.3 19
19.0%
13.4 3
 
3.0%
13.5 1
 
1.0%
13.6 1
 
1.0%
72.7 1
 
1.0%
73.1 1
 
1.0%
73.8 1
 
1.0%
79.6 1
 
1.0%
ValueCountFrequency (%)
104.7 8
8.0%
104.2 1
 
1.0%
103.6 1
 
1.0%
103.1 2
 
2.0%
102.6 1
 
1.0%
102.1 2
 
2.0%
101.9 1
 
1.0%
101.6 4
4.0%
101.1 2
 
2.0%
101.0 5
5.0%

Interactions

2023-12-10T19:47:49.432315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:43.787673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.774190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:45.658023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.585847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.477111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:48.329801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:49.562951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:43.933225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.883206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:45.785865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.715023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.615910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:48.473837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:49.694782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.060926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.992121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:45.923407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.833511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.747921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:48.607754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:49.823908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.194623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:45.092503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.045533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.947411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.844919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:48.729625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:49.962471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.329807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:45.204124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.199886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.080445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.941295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:48.849657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:50.104104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.517792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:45.342360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.337687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.220019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:48.076118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:49.221152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:50.239940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:44.653603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:45.487766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:46.467885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:47.350651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:48.196631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:47:49.326343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:47:53.625575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0660.9880.9250.8350.983
일자/시간(t)0.0001.0000.0000.3460.0000.0000.2740.000
저수위(m)1.0000.0001.0000.0000.8980.9190.9820.909
강우량(mm)0.0660.3460.0001.0000.3630.3740.0000.376
유입량(ms)0.9880.0000.8980.3631.0000.9120.8050.817
방류량(ms)0.9250.0000.9190.3740.9121.0000.8330.881
저수량(백만m3)0.8350.2740.9820.0000.8050.8331.0000.869
저수율0.9830.0000.9090.3760.8170.8810.8691.000
2023-12-10T19:47:54.092141image/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)1.0000.0210.037-0.141-0.167-0.223-0.1360.000
저수위(m)0.0211.000-0.445-0.360-0.385-0.375-0.3090.995
강우량(mm)0.037-0.4451.0000.4490.4630.4310.3920.036
유입량(ms)-0.141-0.3600.4491.0000.9820.9020.9240.835
방류량(ms)-0.167-0.3850.4630.9821.0000.9210.8990.848
저수량(백만m3)-0.223-0.3750.4310.9020.9211.0000.9420.804
저수율-0.136-0.3090.3920.9240.8990.9421.0000.904
댐이름0.0000.9950.0360.8350.8480.8040.9041.000

Missing values

2023-12-10T19:47:50.413600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:47:50.614997image/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
0강천보2021110138.060.0115108.788107.749.0103.1
1강천보2021110238.050.099.3599.8748.954102.6
2강천보2021110338.090.094.76892.6739.135104.7
3강천보2021110438.040.013685.06587.6848.909102.1
4강천보2021110538.060.092.05691.0089.0103.1
5강천보2021110638.030.095.06396.6358.864101.6
6강천보2021110738.030.096.97196.9718.864101.6
7강천보2021110838.0720.5841102.125100.039.045103.6
8강천보2021110938.096.4463114.567113.5199.135104.7
9강천보2021111038.021.0101123.648127.3158.819101.0
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2021110132.650.069.59470.34853.71101.9
91구미보2021110232.590.001767.47872.053.319101.1
92구미보2021110332.590.0559.00859.00853.319101.1
93구미보2021110432.580.053.75454.50853.254101.0
94구미보2021110531.970.036948.111103.36248.4891.9
95구미보2021110631.540.034485.137120.08945.4686.2
96구미보2021110731.040.093.164133.80641.94979.6
97구미보2021110830.5417.301898.755138.06138.55373.1
98구미보2021110930.591.0044119.814115.89538.89173.8
99구미보2021111030.511.0762100.065106.33538.3572.7