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 3 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 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 4 other fieldsHigh correlation
강우량(mm) has 52 (52.0%) 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 10:51:41.818465
Analysis finished2023-12-10 10:51:49.478025
Duration7.66 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

2023-12-10T19:51:49.586377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:51:49.759521image/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%
Mean20200614
Minimum20200601
Maximum20200630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:49.943587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200601
5-th percentile20200602
Q120200607
median20200614
Q320200622
95-th percentile20200629
Maximum20200630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.372875 × 10-7
Kurtosis-1.2329291
Mean20200614
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0200614 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T19:51:50.127865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20200601 4
 
4.0%
20200603 4
 
4.0%
20200604 4
 
4.0%
20200605 4
 
4.0%
20200606 4
 
4.0%
20200607 4
 
4.0%
20200608 4
 
4.0%
20200609 4
 
4.0%
20200610 4
 
4.0%
20200602 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20200601 4
4.0%
20200602 4
4.0%
20200603 4
4.0%
20200604 4
4.0%
20200605 4
4.0%
20200606 4
4.0%
20200607 4
4.0%
20200608 4
4.0%
20200609 4
4.0%
20200610 4
4.0%
ValueCountFrequency (%)
20200630 3
3.0%
20200629 3
3.0%
20200628 3
3.0%
20200627 3
3.0%
20200626 3
3.0%
20200625 3
3.0%
20200624 3
3.0%
20200623 3
3.0%
20200622 3
3.0%
20200621 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.6263
Minimum3.73
Maximum62.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:50.307951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.73
5-th percentile3.7595
Q13.9275
median38.16
Q362.5225
95-th percentile62.57
Maximum62.66
Range58.93
Interquartile range (IQR)58.595

Descriptive statistics

Standard deviation22.947724
Coefficient of variation (CV)0.66272528
Kurtosis-1.3513959
Mean34.6263
Median Absolute Deviation (MAD)24.38
Skewness-0.18825519
Sum3462.63
Variance526.59806
MonotonicityNot monotonic
2023-12-10T19:51:50.520815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.16 7
 
7.0%
38.14 6
 
6.0%
62.57 6
 
6.0%
38.18 5
 
5.0%
62.56 5
 
5.0%
62.54 4
 
4.0%
3.79 3
 
3.0%
3.75 3
 
3.0%
38.24 3
 
3.0%
38.17 3
 
3.0%
Other values (40) 55
55.0%
ValueCountFrequency (%)
3.73 1
 
1.0%
3.74 1
 
1.0%
3.75 3
3.0%
3.76 2
2.0%
3.77 3
3.0%
3.78 1
 
1.0%
3.79 3
3.0%
3.8 2
2.0%
3.81 1
 
1.0%
3.83 1
 
1.0%
ValueCountFrequency (%)
62.66 1
 
1.0%
62.62 1
 
1.0%
62.59 1
 
1.0%
62.58 1
 
1.0%
62.57 6
6.0%
62.56 5
5.0%
62.55 3
3.0%
62.54 4
4.0%
62.53 3
3.0%
62.52 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.757663
Minimum0
Maximum49.249
Zeros52
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:50.743275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.575975
95-th percentile22.18099
Maximum49.249
Range49.249
Interquartile range (IQR)0.575975

Descriptive statistics

Standard deviation7.9415477
Coefficient of variation (CV)2.8798108
Kurtosis16.476423
Mean2.757663
Median Absolute Deviation (MAD)0
Skewness3.900074
Sum275.7663
Variance63.06818
MonotonicityNot monotonic
2023-12-10T19:51:50.983989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 52
52.0%
0.007 2
 
2.0%
0.9504 1
 
1.0%
0.5658 1
 
1.0%
4.442 1
 
1.0%
0.0206 1
 
1.0%
49.249 1
 
1.0%
13.082 1
 
1.0%
2.7577 1
 
1.0%
9.6827 1
 
1.0%
Other values (38) 38
38.0%
ValueCountFrequency (%)
0.0 52
52.0%
0.007 2
 
2.0%
0.0148 1
 
1.0%
0.0154 1
 
1.0%
0.0174 1
 
1.0%
0.0189 1
 
1.0%
0.0206 1
 
1.0%
0.024 1
 
1.0%
0.0266 1
 
1.0%
0.0399 1
 
1.0%
ValueCountFrequency (%)
49.249 1
1.0%
38.3003 1
1.0%
26.139 1
1.0%
25.336 1
1.0%
25.2816 1
1.0%
22.0178 1
1.0%
13.3965 1
1.0%
13.082 1
1.0%
9.6827 1
1.0%
8.0027 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.47802
Minimum0
Maximum451.626
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:51.197470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median80.988
Q3105.02975
95-th percentile151.06
Maximum451.626
Range451.626
Interquartile range (IQR)105.02975

Descriptive statistics

Standard deviation69.791952
Coefficient of variation (CV)0.94983441
Kurtosis9.4304543
Mean73.47802
Median Absolute Deviation (MAD)30.5795
Skewness2.1262168
Sum7347.802
Variance4870.9165
MonotonicityNot monotonic
2023-12-10T19:51:51.389716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
130.866 1
 
1.0%
82.28 1
 
1.0%
80.759 1
 
1.0%
86.757 1
 
1.0%
82.895 1
 
1.0%
82.141 1
 
1.0%
81.511 1
 
1.0%
85.659 1
 
1.0%
114.856 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
36.143 1
 
1.0%
38.686 1
 
1.0%
49.676 1
 
1.0%
51.141 1
 
1.0%
52.082 1
 
1.0%
52.209 1
 
1.0%
53.748 1
 
1.0%
55.405 1
 
1.0%
59.949 1
 
1.0%
ValueCountFrequency (%)
451.626 1
1.0%
345.081 1
1.0%
234.773 1
1.0%
160.118 1
1.0%
151.155 1
1.0%
151.055 1
1.0%
140.074 1
1.0%
137.882 1
1.0%
130.999 1
1.0%
130.866 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.27463
Minimum0
Maximum446.062
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:51.611356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median81.2015
Q3106.00025
95-th percentile146.67355
Maximum446.062
Range446.062
Interquartile range (IQR)106.00025

Descriptive statistics

Standard deviation69.342722
Coefficient of variation (CV)0.94634012
Kurtosis9.0784587
Mean73.27463
Median Absolute Deviation (MAD)33.729
Skewness2.0775236
Sum7327.463
Variance4808.4131
MonotonicityNot monotonic
2023-12-10T19:51:51.838596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
134.009 1
 
1.0%
82.169 1
 
1.0%
80.87 1
 
1.0%
86.868 1
 
1.0%
82.839 1
 
1.0%
82.085 1
 
1.0%
81.567 1
 
1.0%
85.826 1
 
1.0%
115.357 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
41.419 1
 
1.0%
43.918 1
 
1.0%
44.704 1
 
1.0%
47.315 1
 
1.0%
47.415 1
 
1.0%
47.53 1
 
1.0%
53.589 1
 
1.0%
56.417 1
 
1.0%
60.703 1
 
1.0%
ValueCountFrequency (%)
446.062 1
1.0%
339.674 1
1.0%
239.781 1
1.0%
159.673 1
1.0%
151.111 1
1.0%
146.44 1
1.0%
142.073 1
1.0%
135.883 1
1.0%
134.009 1
1.0%
131.556 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.82637
Minimum0
Maximum53.71
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:52.007615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.153
Q39.46325
95-th percentile52.99955
Maximum53.71
Range53.71
Interquartile range (IQR)9.46325

Descriptive statistics

Standard deviation15.335324
Coefficient of variation (CV)1.737444
Kurtosis4.4094734
Mean8.82637
Median Absolute Deviation (MAD)2.153
Skewness2.3957322
Sum882.637
Variance235.17215
MonotonicityNot monotonic
2023-12-10T19:51:52.177938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0 30
30.0%
9.452 7
 
7.0%
9.362 6
 
6.0%
9.543 5
 
5.0%
2.187 3
 
3.0%
2.124 3
 
3.0%
9.497 3
 
3.0%
2.105 3
 
3.0%
2.114 3
 
3.0%
2.153 3
 
3.0%
Other values (28) 34
34.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.095 1
 
1.0%
2.1 1
 
1.0%
2.105 3
 
3.0%
2.11 2
 
2.0%
2.114 3
 
3.0%
2.119 1
 
1.0%
2.124 3
 
3.0%
2.129 2
 
2.0%
2.134 1
 
1.0%
ValueCountFrequency (%)
53.71 1
1.0%
53.58 1
1.0%
53.384 1
1.0%
53.189 1
1.0%
53.124 1
1.0%
52.993 1
1.0%
52.928 2
2.0%
52.863 1
1.0%
52.248 1
1.0%
10.222 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.097
Minimum0
Maximum117.1
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:51:52.552195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.9
Q3107.3
95-th percentile111.93
Maximum117.1
Range117.1
Interquartile range (IQR)107.3

Descriptive statistics

Standard deviation49.748404
Coefficient of variation (CV)1.0562967
Kurtosis-1.8234974
Mean47.097
Median Absolute Deviation (MAD)13.9
Skewness0.38708064
Sum4709.7
Variance2474.9037
MonotonicityNot monotonic
2023-12-10T19:51:52.716585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 30
30.0%
108.3 7
 
7.0%
107.3 6
 
6.0%
13.6 6
 
6.0%
13.7 6
 
6.0%
109.3 5
 
5.0%
14.1 5
 
5.0%
13.5 5
 
5.0%
112.5 3
 
3.0%
13.9 3
 
3.0%
Other values (21) 24
24.0%
ValueCountFrequency (%)
0.0 30
30.0%
13.5 5
 
5.0%
13.6 6
 
6.0%
13.7 6
 
6.0%
13.8 1
 
1.0%
13.9 3
 
3.0%
14.0 1
 
1.0%
14.1 5
 
5.0%
14.2 1
 
1.0%
16.9 1
 
1.0%
ValueCountFrequency (%)
117.1 1
 
1.0%
116.6 1
 
1.0%
112.5 3
3.0%
111.9 1
 
1.0%
111.4 1
 
1.0%
109.3 5
5.0%
108.8 3
3.0%
108.3 7
7.0%
107.8 1
 
1.0%
107.3 6
6.0%

Interactions

2023-12-10T19:51:47.900644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:42.170007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:43.188001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:44.186056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.162631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:46.080108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:47.046238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:48.036785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:42.320455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:43.362497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:44.348693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.316020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:46.230788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:47.199463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:48.415176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:42.472309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:43.501197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:44.493729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.439192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:46.370750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:47.311416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:48.552535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:42.618150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:43.630351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:44.634592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.550515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:46.503770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:47.443055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:48.696330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:42.756269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:43.763566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:44.762612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.650134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:46.641385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:47.569623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:48.812515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:42.886008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:43.897559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:44.881733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.778237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:46.765144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:47.683701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:49.029477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:43.038093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:44.034752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.019668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:45.937170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:46.904636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:51:47.787534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:51:52.877557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.8120.8111.0001.000
일자/시간(t)0.0001.0000.0000.3420.2830.2750.0000.000
저수위(m)1.0000.0001.0000.0000.8120.8111.0001.000
강우량(mm)0.0000.3420.0001.0000.7410.7380.0000.000
유입량(ms)0.8120.2830.8120.7411.0001.0000.7050.812
방류량(ms)0.8110.2750.8110.7381.0001.0000.7030.811
저수량(백만m3)1.0000.0001.0000.0000.7050.7031.0001.000
저수율1.0000.0001.0000.0000.8120.8111.0001.000
2023-12-10T19:51:53.044461image/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)1.0000.1200.0650.0860.087-0.151-0.0320.000
저수위(m)0.1201.000-0.353-0.451-0.456-0.365-0.2931.000
강우량(mm)0.065-0.3531.0000.6490.6410.5060.5880.000
유입량(ms)0.086-0.4510.6491.0000.9980.6800.8380.705
방류량(ms)0.087-0.4560.6410.9981.0000.6750.8330.703
저수량(백만m3)-0.151-0.3650.5060.6800.6751.0000.9260.995
저수율-0.032-0.2930.5880.8380.8330.9261.0001.000
댐이름0.0001.0000.0000.7050.7030.9951.0001.000

Missing values

2023-12-10T19:51:49.204388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:51:49.397536image/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
0강천보2020060138.140.9504130.866134.0099.362107.3
1강천보2020060238.140.2061102.958102.9589.362107.3
2강천보2020060338.140.4683106.865106.8659.362107.3
3강천보2020060438.140.0116.158116.1589.362107.3
4강천보2020060538.170.0154105.317103.7459.497108.8
5강천보2020060638.160.0189101.815102.3399.452108.3
6강천보2020060738.150.098.73599.2599.407107.8
7강천보2020060838.160.007100.993100.4699.452108.3
8강천보2020060938.160.007102.098102.0989.452108.3
9강천보2020061038.1625.2816102.592102.5929.452108.3
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2020060132.440.119538.68647.31552.24899.1
91구미보2020060232.530.051755.40547.5352.928100.4
92구미보2020060332.520.059.94960.70352.863100.3
93구미보2020060432.630.052.20943.91853.58101.6
94구미보2020060532.560.041151.14156.41753.124100.7
95구미보2020060632.540.052.08253.58952.993100.5
96구미보2020060732.570.049.67647.41553.189100.9
97구미보2020060832.60.071.50969.24853.384101.2
98구미보2020060932.530.036.14341.41952.928100.4
99구미보2020061032.6513.396553.74844.70453.71101.9