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 저수량(백만m3) and 1 other fieldsHigh 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 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 5 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 2 other fieldsHigh correlation
강우량(mm) has 51 (51.0%) 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 10:46:41.983903
Analysis finished2023-12-10 10:46:49.414002
Duration7.43 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

2023-12-10T19:46:49.525476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:46:49.704613image/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%
Mean20220614
Minimum20220601
Maximum20220630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:46:49.893333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220601
5-th percentile20220602
Q120220607
median20220614
Q320220622
95-th percentile20220629
Maximum20220630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3685499 × 10-7
Kurtosis-1.2329291
Mean20220614
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0220614 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T19:46:50.080420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20220601 4
 
4.0%
20220603 4
 
4.0%
20220604 4
 
4.0%
20220605 4
 
4.0%
20220606 4
 
4.0%
20220607 4
 
4.0%
20220608 4
 
4.0%
20220609 4
 
4.0%
20220610 4
 
4.0%
20220602 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20220601 4
4.0%
20220602 4
4.0%
20220603 4
4.0%
20220604 4
4.0%
20220605 4
4.0%
20220606 4
4.0%
20220607 4
4.0%
20220608 4
4.0%
20220609 4
4.0%
20220610 4
4.0%
ValueCountFrequency (%)
20220630 3
3.0%
20220629 3
3.0%
20220628 3
3.0%
20220627 3
3.0%
20220626 3
3.0%
20220625 3
3.0%
20220624 3
3.0%
20220623 3
3.0%
20220622 3
3.0%
20220621 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.484723
Minimum3.69
Maximum60.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:46:50.264583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.69
5-th percentile3.7019
Q17.5095
median38.05
Q360.4
95-th percentile60.4
Maximum60.4
Range56.71
Interquartile range (IQR)52.8905

Descriptive statistics

Standard deviation21.479435
Coefficient of variation (CV)0.62286812
Kurtosis-1.3351489
Mean34.484723
Median Absolute Deviation (MAD)22.35
Skewness-0.21511493
Sum3448.4723
Variance461.36611
MonotonicityNot monotonic
2023-12-10T19:46:50.488412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
60.4 30
30.0%
38.05 6
 
6.0%
38.03 4
 
4.0%
38.04 3
 
3.0%
38.07 3
 
3.0%
38.06 3
 
3.0%
3.72 3
 
3.0%
3.7 3
 
3.0%
32.53 2
 
2.0%
7.47 2
 
2.0%
Other values (35) 41
41.0%
ValueCountFrequency (%)
3.69 2
2.0%
3.7 3
3.0%
3.702 2
2.0%
3.71 2
2.0%
3.72 3
3.0%
3.74 2
2.0%
6.12 1
 
1.0%
6.255 1
 
1.0%
6.87 1
 
1.0%
7.4 1
 
1.0%
ValueCountFrequency (%)
60.4 30
30.0%
38.28 1
 
1.0%
38.25 1
 
1.0%
38.2 1
 
1.0%
38.17 1
 
1.0%
38.14 1
 
1.0%
38.122 1
 
1.0%
38.12 2
 
2.0%
38.09 2
 
2.0%
38.08 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.875355
Minimum0
Maximum71.1593
Zeros51
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:46:50.746005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.7268
95-th percentile29.776635
Maximum71.1593
Range71.1593
Interquartile range (IQR)0.7268

Descriptive statistics

Standard deviation11.04439
Coefficient of variation (CV)2.849904
Kurtosis16.695685
Mean3.875355
Median Absolute Deviation (MAD)0
Skewness3.8473288
Sum387.5355
Variance121.97854
MonotonicityNot monotonic
2023-12-10T19:46:50.924684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 51
51.0%
12.4983 1
 
1.0%
0.6474 1
 
1.0%
0.004 1
 
1.0%
3.0633 1
 
1.0%
17.7442 1
 
1.0%
0.0123 1
 
1.0%
0.0162 1
 
1.0%
0.0041 1
 
1.0%
0.0082 1
 
1.0%
Other values (40) 40
40.0%
ValueCountFrequency (%)
0.0 51
51.0%
0.004 1
 
1.0%
0.0041 1
 
1.0%
0.0081 1
 
1.0%
0.0082 1
 
1.0%
0.0084 1
 
1.0%
0.0119 1
 
1.0%
0.0123 1
 
1.0%
0.0162 1
 
1.0%
0.0323 1
 
1.0%
ValueCountFrequency (%)
71.1593 1
1.0%
44.2623 1
1.0%
41.6615 1
1.0%
35.5373 1
1.0%
31.4493 1
1.0%
29.6886 1
1.0%
22.5803 1
1.0%
17.7442 1
1.0%
15.3104 1
1.0%
14.7355 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.01799
Minimum0
Maximum1072.621
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:46:51.092334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60.805
Q391.28875
95-th percentile143.7366
Maximum1072.621
Range1072.621
Interquartile range (IQR)91.28875

Descriptive statistics

Standard deviation116.47078
Coefficient of variation (CV)1.6172456
Kurtosis56.023718
Mean72.01799
Median Absolute Deviation (MAD)33.16
Skewness6.7141572
Sum7201.799
Variance13565.442
MonotonicityNot monotonic
2023-12-10T19:46:51.533511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
85.88 2
 
2.0%
143.144 1
 
1.0%
73.098 1
 
1.0%
81.481 1
 
1.0%
94.91 1
 
1.0%
91.78 1
 
1.0%
96.992 1
 
1.0%
108.089 1
 
1.0%
101.599 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 30
30.0%
43.139 1
 
1.0%
46.184 1
 
1.0%
46.369 1
 
1.0%
46.448 1
 
1.0%
46.541 1
 
1.0%
47.258 1
 
1.0%
47.374 1
 
1.0%
47.78 1
 
1.0%
48.534 1
 
1.0%
ValueCountFrequency (%)
1072.621 1
1.0%
376.381 1
1.0%
274.699 1
1.0%
160.275 1
1.0%
154.996 1
1.0%
143.144 1
1.0%
128.461 1
1.0%
112.55 1
1.0%
111.256 1
1.0%
110.965 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.00418
Minimum0
Maximum1068.43
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:46:51.728387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median58.348
Q390.64925
95-th percentile146.97915
Maximum1068.43
Range1068.43
Interquartile range (IQR)90.64925

Descriptive statistics

Standard deviation115.95688
Coefficient of variation (CV)1.6330993
Kurtosis56.317418
Mean71.00418
Median Absolute Deviation (MAD)33.8215
Skewness6.741192
Sum7100.418
Variance13445.997
MonotonicityNot monotonic
2023-12-10T19:46:51.931325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
91.047 1
 
1.0%
146.752 1
 
1.0%
76.244 1
 
1.0%
75.56 1
 
1.0%
94.91 1
 
1.0%
91.965 1
 
1.0%
69.884 1
 
1.0%
78.202 1
 
1.0%
106.979 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
43.028 1
 
1.0%
43.741 1
 
1.0%
46.358 1
 
1.0%
46.485 1
 
1.0%
46.504 1
 
1.0%
46.755 1
 
1.0%
47.202 1
 
1.0%
47.318 1
 
1.0%
47.891 1
 
1.0%
ValueCountFrequency (%)
1068.43 1
1.0%
376.288 1
1.0%
266.318 1
1.0%
164.466 1
1.0%
151.295 1
1.0%
146.752 1
1.0%
129.756 1
1.0%
114.108 1
1.0%
111.361 1
1.0%
109.931 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.833862
Minimum0
Maximum53.7098
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:46:52.169316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.8638
Q39.68285
95-th percentile52.931655
Maximum53.7098
Range53.7098
Interquartile range (IQR)9.68285

Descriptive statistics

Standard deviation15.047809
Coefficient of variation (CV)1.5302034
Kurtosis4.2299641
Mean9.833862
Median Absolute Deviation (MAD)6.7735
Skewness2.3227301
Sum983.3862
Variance226.43655
MonotonicityNot monotonic
2023-12-10T19:46:52.387560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 30
30.0%
8.9543 6
 
6.0%
8.8638 4
 
4.0%
8.909 3
 
3.0%
9.0448 3
 
3.0%
8.9996 3
 
3.0%
2.0903 3
 
3.0%
2.0807 3
 
3.0%
52.9284 2
 
2.0%
10.1116 2
 
2.0%
Other values (35) 41
41.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.0759 2
 
2.0%
2.0807 3
 
3.0%
2.0817 2
 
2.0%
2.0855 2
 
2.0%
2.0903 3
 
3.0%
2.0999 2
 
2.0%
5.2032 1
 
1.0%
5.668 1
 
1.0%
7.7854 1
 
1.0%
ValueCountFrequency (%)
53.7098 1
1.0%
53.3191 1
1.0%
53.1237 1
1.0%
53.0586 1
1.0%
52.9935 1
1.0%
52.9284 2
2.0%
52.8632 1
1.0%
52.733 1
1.0%
52.0863 1
1.0%
10.6232 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.9762
Minimum0
Maximum114.52
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:46:52.608294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median64.01
Q3102.07
95-th percentile106.3765
Maximum114.52
Range114.52
Interquartile range (IQR)102.07

Descriptive statistics

Standard deviation46.194667
Coefficient of variation (CV)0.87198905
Kurtosis-1.8236208
Mean52.9762
Median Absolute Deviation (MAD)42.265
Skewness-0.0095107215
Sum5297.62
Variance2133.9472
MonotonicityNot monotonic
2023-12-10T19:46:52.836270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0 30
30.0%
13.39 5
 
5.0%
102.59 5
 
5.0%
101.56 4
 
4.0%
102.07 3
 
3.0%
13.45 3
 
3.0%
13.51 2
 
2.0%
13.36 2
 
2.0%
100.37 2
 
2.0%
106.22 2
 
2.0%
Other values (37) 42
42.0%
ValueCountFrequency (%)
0.0 30
30.0%
13.36 2
 
2.0%
13.39 5
 
5.0%
13.42 2
 
2.0%
13.45 3
 
3.0%
13.51 2
 
2.0%
33.48 1
 
1.0%
36.47 1
 
1.0%
50.1 1
 
1.0%
63.26 1
 
1.0%
ValueCountFrequency (%)
114.52 1
1.0%
112.96 1
1.0%
110.37 1
1.0%
108.82 1
1.0%
107.26 1
1.0%
106.33 1
1.0%
106.22 2
2.0%
104.67 2
2.0%
104.15 1
1.0%
103.63 2
2.0%

Interactions

2023-12-10T19:46:48.092514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:42.295007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:43.198058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:44.279780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:45.410569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:46.752627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.460546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:48.204153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:42.432239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:43.324759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:44.421097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:45.578356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:46.880504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.546445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:48.338309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:42.555903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:43.469308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:44.565340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:45.742940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:46.972818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.627647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:48.504167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:42.694590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:43.612634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:44.732450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:45.925343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.080119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.767250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:48.658767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:42.833791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:43.747980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:44.875052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:46.340685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.163189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.852552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:48.777179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:42.967642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:43.888220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:45.043121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:46.468784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.255089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.931488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:48.908325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:43.080488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:44.019526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:45.209187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:46.622827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:47.355842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:46:48.008301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:46:53.006296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.1110.1110.8400.988
일자/시간(t)0.0001.0000.0000.3090.5180.5180.2620.328
저수위(m)1.0000.0001.0000.0000.1110.1110.8400.988
강우량(mm)0.0000.3090.0001.0000.8860.8860.2090.533
유입량(ms)0.1110.5180.1110.8861.0001.0000.2710.352
방류량(ms)0.1110.5180.1110.8861.0001.0000.1930.352
저수량(백만m3)0.8400.2620.8400.2090.2710.1931.0000.844
저수율0.9880.3280.9880.5330.3520.3520.8441.000
2023-12-10T19:46:53.216221image/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)1.0000.1900.2170.2560.2370.0750.0670.000
저수위(m)0.1901.000-0.406-0.434-0.420-0.518-0.2961.000
강우량(mm)0.217-0.4061.0000.6250.6080.5430.5570.000
유입량(ms)0.256-0.4340.6251.0000.9760.7570.8610.088
방류량(ms)0.237-0.4200.6080.9761.0000.7640.8740.088
저수량(백만m3)0.075-0.5180.5430.7570.7641.0000.7800.905
저수율0.067-0.2960.5570.8610.8740.7801.0000.836
댐이름0.0001.0000.0000.0880.0880.9050.8361.000

Missing values

2023-12-10T19:46:49.094423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:46:49.322177image/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
0강천보2022060138.040.090.52391.0478.909102.07
1강천보2022060238.050.091.12590.6018.9543102.59
2강천보2022060338.030.089.57790.6258.8638101.56
3강천보2022060438.040.090.82190.2978.909102.07
4강천보2022060538.042.880390.72290.7228.909102.07
5강천보2022060638.079.023993.94692.3749.0448103.6
6강천보2022060738.060.032393.25393.7778.9996103.1
7강천보2022060838.070.048193.98493.469.0448103.63
8강천보2022060938.060.779989.08889.6128.9996103.11
9강천보2022061038.050.059387.90188.4258.9543102.59
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2022060132.520.046.18443.74152.8632100.25
91구미보2022060232.550.049.01646.75553.0586100.62
92구미보2022060332.530.054.25255.75952.9284100.37
93구미보2022060432.530.055.51455.51452.9284100.37
94구미보2022060532.5915.310460.73256.2153.3191101.11
95구미보2022060632.656.466184.58480.06253.7098101.85
96구미보2022060732.420.371970.84489.63552.086398.77
97구미보2022060832.560.116672.49360.48653.1237100.74
98구미보2022060932.540.167460.87862.38552.9935100.49
99구미보2022061032.50.099259.90562.9252.733100.0