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 65 (65.0%) 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 10:52:48.716819
Analysis finished2023-12-10 10:52:57.117239
Duration8.4 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

2023-12-10T19:52:57.215637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:52:57.385531image/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%
Mean20200115
Minimum20200101
Maximum20200131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:57.563269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200101
5-th percentile20200102
Q120200107
median20200115
Q320200123
95-th percentile20200130
Maximum20200131
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5615116 × 10-7
Kurtosis-1.2602055
Mean20200115
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0200115 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T19:52:57.769539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20200101 4
 
4.0%
20200103 4
 
4.0%
20200104 4
 
4.0%
20200105 4
 
4.0%
20200106 4
 
4.0%
20200107 4
 
4.0%
20200102 4
 
4.0%
20200126 3
 
3.0%
20200122 3
 
3.0%
20200123 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20200101 4
4.0%
20200102 4
4.0%
20200103 4
4.0%
20200104 4
4.0%
20200105 4
4.0%
20200106 4
4.0%
20200107 4
4.0%
20200108 3
3.0%
20200109 3
3.0%
20200110 3
3.0%
ValueCountFrequency (%)
20200131 3
3.0%
20200130 3
3.0%
20200129 3
3.0%
20200128 3
3.0%
20200127 3
3.0%
20200126 3
3.0%
20200125 3
3.0%
20200124 3
3.0%
20200123 3
3.0%
20200122 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8308
Minimum4.29
Maximum62.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:57.973988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.29
5-th percentile4.32
Q14.3775
median38.095
Q362.46
95-th percentile62.4805
Maximum62.59
Range58.3
Interquartile range (IQR)58.0825

Descriptive statistics

Standard deviation23.085232
Coefficient of variation (CV)0.66278214
Kurtosis-1.404699
Mean34.8308
Median Absolute Deviation (MAD)24.365
Skewness-0.18243607
Sum3483.08
Variance532.92794
MonotonicityNot monotonic
2023-12-10T19:52:58.185592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
62.46 10
 
10.0%
62.47 10
 
10.0%
4.34 9
 
9.0%
38.09 7
 
7.0%
38.11 7
 
7.0%
38.1 5
 
5.0%
4.33 4
 
4.0%
4.35 4
 
4.0%
32.58 4
 
4.0%
4.3 3
 
3.0%
Other values (32) 37
37.0%
ValueCountFrequency (%)
4.29 1
 
1.0%
4.3 3
 
3.0%
4.32 2
 
2.0%
4.33 4
4.0%
4.34 9
9.0%
4.35 4
4.0%
4.36 1
 
1.0%
4.37 1
 
1.0%
4.38 1
 
1.0%
4.48 1
 
1.0%
ValueCountFrequency (%)
62.59 1
 
1.0%
62.53 1
 
1.0%
62.5 1
 
1.0%
62.49 2
 
2.0%
62.48 2
 
2.0%
62.47 10
10.0%
62.46 10
10.0%
62.45 3
 
3.0%
62.42 1
 
1.0%
38.46 1
 
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.819073
Minimum0
Maximum50.4135
Zeros65
Zeros (%)65.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:58.403418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01695
95-th percentile7.85869
Maximum50.4135
Range50.4135
Interquartile range (IQR)0.01695

Descriptive statistics

Standard deviation7.9642054
Coefficient of variation (CV)4.378167
Kurtosis29.579265
Mean1.819073
Median Absolute Deviation (MAD)0
Skewness5.4170457
Sum181.9073
Variance63.428567
MonotonicityNot monotonic
2023-12-10T19:52:58.570085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 65
65.0%
0.0235 3
 
3.0%
0.0014 3
 
3.0%
0.0028 2
 
2.0%
6.7682 1
 
1.0%
35.9333 1
 
1.0%
7.8813 1
 
1.0%
0.0163 1
 
1.0%
0.0655 1
 
1.0%
3.3233 1
 
1.0%
Other values (21) 21
 
21.0%
ValueCountFrequency (%)
0.0 65
65.0%
0.0014 3
 
3.0%
0.0028 2
 
2.0%
0.004 1
 
1.0%
0.0042 1
 
1.0%
0.0112 1
 
1.0%
0.0119 1
 
1.0%
0.0163 1
 
1.0%
0.0189 1
 
1.0%
0.023 1
 
1.0%
ValueCountFrequency (%)
50.4135 1
1.0%
49.8236 1
1.0%
35.9333 1
1.0%
8.9142 1
1.0%
7.8813 1
1.0%
7.8575 1
1.0%
6.7682 1
1.0%
4.1861 1
1.0%
3.3531 1
1.0%
3.3233 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.1336
Minimum0
Maximum666.989
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:58.791181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40.586
Q394.745
95-th percentile255.3582
Maximum666.989
Range666.989
Interquartile range (IQR)94.745

Descriptive statistics

Standard deviation106.24347
Coefficient of variation (CV)1.4728708
Kurtosis14.277563
Mean72.1336
Median Absolute Deviation (MAD)40.586
Skewness3.4131813
Sum7213.36
Variance11287.675
MonotonicityNot monotonic
2023-12-10T19:52:59.032692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
40.368 1
 
1.0%
39.388 1
 
1.0%
41.187 1
 
1.0%
41.139 1
 
1.0%
40.804 1
 
1.0%
44.831 1
 
1.0%
42.541 1
 
1.0%
51.714 1
 
1.0%
90.289 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
32.32 1
 
1.0%
35.046 1
 
1.0%
35.357 1
 
1.0%
37.255 1
 
1.0%
37.425 1
 
1.0%
37.833 1
 
1.0%
38.027 1
 
1.0%
38.185 1
 
1.0%
38.236 1
 
1.0%
ValueCountFrequency (%)
666.989 1
1.0%
525.137 1
1.0%
495.866 1
1.0%
299.844 1
1.0%
273.564 1
1.0%
254.4 1
1.0%
187.343 1
1.0%
158.292 1
1.0%
149.529 1
1.0%
137.762 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.99636
Minimum0
Maximum662.087
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:59.237771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40.3695
Q394.745
95-th percentile257.3681
Maximum662.087
Range662.087
Interquartile range (IQR)94.745

Descriptive statistics

Standard deviation106.29712
Coefficient of variation (CV)1.4764236
Kurtosis14.09735
Mean71.99636
Median Absolute Deviation (MAD)40.3695
Skewness3.4008111
Sum7199.636
Variance11299.078
MonotonicityNot monotonic
2023-12-10T19:52:59.470817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
40.132 1
 
1.0%
39.467 1
 
1.0%
41.345 1
 
1.0%
40.981 1
 
1.0%
40.883 1
 
1.0%
44.752 1
 
1.0%
42.62 1
 
1.0%
51.635 1
 
1.0%
90.813 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
30.813 1
 
1.0%
32.785 1
 
1.0%
35.515 1
 
1.0%
37.176 1
 
1.0%
37.504 1
 
1.0%
38.027 1
 
1.0%
38.15 1
 
1.0%
38.301 1
 
1.0%
38.472 1
 
1.0%
ValueCountFrequency (%)
662.087 1
1.0%
529.33 1
1.0%
497.127 1
1.0%
290.415 1
1.0%
289.803 1
1.0%
255.661 1
1.0%
176.342 1
1.0%
155.06 1
1.0%
151.101 1
1.0%
147.715 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.38942
Minimum0
Maximum54.231
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:59.682380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.46
Q39.181
95-th percentile53.19225
Maximum54.231
Range54.231
Interquartile range (IQR)9.181

Descriptive statistics

Standard deviation13.237166
Coefficient of variation (CV)1.7913673
Kurtosis7.8826556
Mean7.38942
Median Absolute Deviation (MAD)2.46
Skewness2.9530503
Sum738.942
Variance175.22255
MonotonicityNot monotonic
2023-12-10T19:52:59.854336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.0 31
31.0%
2.457 9
 
9.0%
9.135 7
 
7.0%
9.226 7
 
7.0%
9.181 5
 
5.0%
53.254 4
 
4.0%
2.463 4
 
4.0%
2.45 4
 
4.0%
2.429 3
 
3.0%
2.443 2
 
2.0%
Other values (24) 24
24.0%
ValueCountFrequency (%)
0.0 31
31.0%
2.422 1
 
1.0%
2.429 3
 
3.0%
2.443 2
 
2.0%
2.45 4
 
4.0%
2.457 9
 
9.0%
2.463 4
 
4.0%
2.47 1
 
1.0%
2.477 1
 
1.0%
2.484 1
 
1.0%
ValueCountFrequency (%)
54.231 1
 
1.0%
53.254 4
4.0%
53.189 1
 
1.0%
53.124 1
 
1.0%
10.81 1
 
1.0%
10.357 1
 
1.0%
9.995 1
 
1.0%
9.497 1
 
1.0%
9.407 1
 
1.0%
9.362 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.053
Minimum0
Maximum123.9
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:00.035980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.85
Q3104.7
95-th percentile107.325
Maximum123.9
Range123.9
Interquartile range (IQR)104.7

Descriptive statistics

Standard deviation48.009051
Coefficient of variation (CV)1.0656127
Kurtosis-1.7313289
Mean45.053
Median Absolute Deviation (MAD)15.85
Skewness0.45875079
Sum4505.3
Variance2304.869
MonotonicityNot monotonic
2023-12-10T19:53:00.195224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 31
31.0%
15.8 13
13.0%
105.7 7
 
7.0%
104.7 7
 
7.0%
15.9 6
 
6.0%
101.0 5
 
5.0%
105.2 5
 
5.0%
15.6 4
 
4.0%
15.7 2
 
2.0%
20.2 1
 
1.0%
Other values (19) 19
19.0%
ValueCountFrequency (%)
0.0 31
31.0%
15.6 4
 
4.0%
15.7 2
 
2.0%
15.8 13
13.0%
15.9 6
 
6.0%
16.0 1
 
1.0%
16.4 1
 
1.0%
17.1 1
 
1.0%
17.4 1
 
1.0%
17.8 1
 
1.0%
ValueCountFrequency (%)
123.9 1
 
1.0%
118.7 1
 
1.0%
114.5 1
 
1.0%
108.8 1
 
1.0%
107.8 1
 
1.0%
107.3 1
 
1.0%
106.7 1
 
1.0%
105.7 7
7.0%
105.2 5
5.0%
104.7 7
7.0%

Interactions

2023-12-10T19:52:55.522825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:49.146255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:50.542709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:51.516571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:52.618191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:53.625262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.578617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:55.673552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:49.315027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:50.693770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:51.672801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:52.772437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:53.750414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.727374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:55.819522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:49.480931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:50.831592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:51.818463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:52.918026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:53.900015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.860536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:55.946445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:49.657774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:50.965809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:52.040967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:53.065432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.036917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.991949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:56.075223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:49.821557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:51.098878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:52.201826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:53.242638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.166851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:55.124800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:56.483798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:49.967552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:51.226396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:52.348384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:53.387877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.304012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:55.256824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:56.630016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:50.382232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:51.352867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:52.475813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:53.516846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:54.442060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:55.389841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:53:00.338322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.4370.6880.6881.0000.981
일자/시간(t)0.0001.0000.0000.2720.4360.4360.1390.000
저수위(m)1.0000.0001.0000.4370.6880.6881.0000.981
강우량(mm)0.4370.2720.4371.0000.1760.1760.2530.569
유입량(ms)0.6880.4360.6880.1761.0001.0000.7440.662
방류량(ms)0.6880.4360.6880.1761.0001.0000.7440.662
저수량(백만m3)1.0000.1391.0000.2530.7440.7441.0000.673
저수율0.9810.0000.9810.5690.6620.6620.6731.000
2023-12-10T19:53:00.546418image/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)1.0000.057-0.0040.0140.009-0.165-0.0630.000
저수위(m)0.0571.000-0.394-0.429-0.430-0.371-0.3231.000
강우량(mm)-0.004-0.3941.0000.3390.3430.4260.3790.181
유입량(ms)0.014-0.4290.3391.0000.9990.7840.8860.544
방류량(ms)0.009-0.4300.3430.9991.0000.7840.8850.544
저수량(백만m3)-0.165-0.3710.4260.7840.7841.0000.9510.995
저수율-0.063-0.3230.3790.8860.8850.9511.0000.814
댐이름0.0001.0000.1810.5440.5440.9950.8141.000

Missing values

2023-12-10T19:52:56.831241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:52:57.040881image/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
0강천보2020010138.096.768290.28990.8139.135104.7
1강천보2020010238.10.089.8589.3269.181105.2
2강천보2020010338.090.090.35690.889.135104.7
3강천보2020010438.090.090.65890.6589.135104.7
4강천보2020010538.090.090.64990.6499.135104.7
5강천보2020010638.093.353190.08690.0869.135104.7
6강천보2020010738.2850.4135108.83698.8839.995114.5
7강천보2020010838.468.9142299.844290.41510.81123.9
8강천보2020010938.150.0189273.564289.8039.407107.8
9강천보2020011038.360.0187.343176.34210.357118.7
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구담보2020012962.470.00.00.00.00.0
91구담보2020013062.470.00.00.00.00.0
92구담보2020013162.470.00.00.00.00.0
93구미보2020010132.580.023535.04632.78553.254101.0
94구미보2020010232.560.023538.18539.69253.124100.7
95구미보2020010332.580.023532.3230.81353.254101.0
96구미보2020010432.570.037.83338.58753.189100.9
97구미보2020010532.580.038.90438.1553.254101.0
98구미보2020010632.587.881338.02738.02753.254101.0
99구미보2020010732.7335.933364.51653.2154.231102.8