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 9 (9.0%) zerosZeros
방류량(ms) has 10 (10.0%) zerosZeros
저수량(백만m3) has 9 (9.0%) zerosZeros
저수율 has 9 (9.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:49:44.743194
Analysis finished2023-12-10 10:49:52.753824
Duration8.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
귤현보
19 
강천보
14 
공주보
14 
달성보
14 
구미보
13 
Other values (3)
26 

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 (%)
귤현보 19
19.0%
강천보 14
14.0%
공주보 14
14.0%
달성보 14
14.0%
구미보 13
13.0%
낙단보 12
12.0%
구담보 9
9.0%
백제보 5
 
5.0%

Length

2023-12-10T19:49:52.870531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:53.056047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
귤현보 19
19.0%
강천보 14
14.0%
공주보 14
14.0%
달성보 14
14.0%
구미보 13
13.0%
낙단보 12
12.0%
구담보 9
9.0%
백제보 5
 
5.0%

일자/시간(t)
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210214
Minimum20210201
Maximum20210228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:53.312788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210201
5-th percentile20210202
Q120210208
median20210214
Q320210221
95-th percentile20210226
Maximum20210228
Range27
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation7.9197745
Coefficient of variation (CV)3.9186989 × 10-7
Kurtosis-1.2219856
Mean20210214
Median Absolute Deviation (MAD)6.5
Skewness-0.042502257
Sum2.0210214 × 109
Variance62.722828
MonotonicityNot monotonic
2023-12-10T19:49:53.557469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20210222 6
 
6.0%
20210206 6
 
6.0%
20210211 5
 
5.0%
20210212 5
 
5.0%
20210219 5
 
5.0%
20210208 5
 
5.0%
20210225 5
 
5.0%
20210221 5
 
5.0%
20210216 4
 
4.0%
20210224 4
 
4.0%
Other values (18) 50
50.0%
ValueCountFrequency (%)
20210201 3
3.0%
20210202 4
4.0%
20210203 4
4.0%
20210204 3
3.0%
20210205 2
 
2.0%
20210206 6
6.0%
20210207 3
3.0%
20210208 5
5.0%
20210209 2
 
2.0%
20210210 3
3.0%
ValueCountFrequency (%)
20210228 3
3.0%
20210227 1
 
1.0%
20210226 3
3.0%
20210225 5
5.0%
20210224 4
4.0%
20210223 2
 
2.0%
20210222 6
6.0%
20210221 5
5.0%
20210220 3
3.0%
20210219 5
5.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.0798
Minimum2.84
Maximum63.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:53.791892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.84
5-th percentile3.1585
Q13.63
median13.695
Q338.03
95-th percentile62.45
Maximum63.23
Range60.39
Interquartile range (IQR)34.4

Descriptive statistics

Standard deviation19.488471
Coefficient of variation (CV)0.84439515
Kurtosis-0.90495995
Mean23.0798
Median Absolute Deviation (MAD)10.815
Skewness0.50284788
Sum2307.98
Variance379.80051
MonotonicityNot monotonic
2023-12-10T19:49:54.042663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.45 8
 
8.0%
38.03 8
 
8.0%
3.64 7
 
7.0%
3.19 4
 
4.0%
38.02 3
 
3.0%
3.63 3
 
3.0%
3.65 3
 
3.0%
39.76 3
 
3.0%
32.64 2
 
2.0%
2.88 2
 
2.0%
Other values (49) 57
57.0%
ValueCountFrequency (%)
2.84 1
 
1.0%
2.88 2
2.0%
2.9 1
 
1.0%
2.94 1
 
1.0%
3.17 1
 
1.0%
3.18 1
 
1.0%
3.19 4
4.0%
3.2 1
 
1.0%
3.21 2
2.0%
3.22 1
 
1.0%
ValueCountFrequency (%)
63.23 1
 
1.0%
62.45 8
8.0%
39.89 1
 
1.0%
39.86 1
 
1.0%
39.79 2
 
2.0%
39.77 1
 
1.0%
39.76 3
 
3.0%
39.69 1
 
1.0%
39.67 1
 
1.0%
39.61 1
 
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.258097
Minimum0
Maximum8.3402
Zeros59
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:54.264660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.07175
95-th percentile1.09262
Maximum8.3402
Range8.3402
Interquartile range (IQR)0.07175

Descriptive statistics

Standard deviation1.0618859
Coefficient of variation (CV)4.1142898
Kurtosis44.159134
Mean0.258097
Median Absolute Deviation (MAD)0
Skewness6.4675406
Sum25.8097
Variance1.1276016
MonotonicityNot monotonic
2023-12-10T19:49:54.503320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.0 59
59.0%
0.2554 1
 
1.0%
0.2601 1
 
1.0%
0.2701 1
 
1.0%
0.0163 1
 
1.0%
0.2078 1
 
1.0%
0.0765 1
 
1.0%
0.0139 1
 
1.0%
1.0987 1
 
1.0%
0.0002 1
 
1.0%
Other values (32) 32
32.0%
ValueCountFrequency (%)
0.0 59
59.0%
0.0002 1
 
1.0%
0.0006 1
 
1.0%
0.002 1
 
1.0%
0.004 1
 
1.0%
0.0045 1
 
1.0%
0.008 1
 
1.0%
0.0138 1
 
1.0%
0.0139 1
 
1.0%
0.0163 1
 
1.0%
ValueCountFrequency (%)
8.3402 1
1.0%
6.2921 1
1.0%
1.5928 1
1.0%
1.2986 1
1.0%
1.0987 1
1.0%
1.0923 1
1.0%
0.7956 1
1.0%
0.6589 1
1.0%
0.6338 1
1.0%
0.4609 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.77359
Minimum0
Maximum86.927
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:54.739107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.87725
median38.987
Q353.661
95-th percentile78.08285
Maximum86.927
Range86.927
Interquartile range (IQR)42.78375

Descriptive statistics

Standard deviation22.875758
Coefficient of variation (CV)0.62207029
Kurtosis-0.73742791
Mean36.77359
Median Absolute Deviation (MAD)17.4565
Skewness0.06871952
Sum3677.359
Variance523.30029
MonotonicityNot monotonic
2023-12-10T19:49:54.986439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
80.488 1
 
1.0%
10.017 1
 
1.0%
41.964 1
 
1.0%
44.584 1
 
1.0%
36.9 1
 
1.0%
38.057 1
 
1.0%
42.987 1
 
1.0%
35.405 1
 
1.0%
26.543 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 9
9.0%
8.809 1
 
1.0%
9.783 1
 
1.0%
9.784 1
 
1.0%
9.82 1
 
1.0%
9.887 1
 
1.0%
10.017 1
 
1.0%
10.036 1
 
1.0%
10.042 1
 
1.0%
10.064 1
 
1.0%
ValueCountFrequency (%)
86.927 1
1.0%
83.585 1
1.0%
81.849 1
1.0%
80.488 1
1.0%
79.6 1
1.0%
78.003 1
1.0%
68.108 1
1.0%
66.948 1
1.0%
66.933 1
1.0%
65.381 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.11613
Minimum0
Maximum88.196
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:55.240162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.51725
median38.753
Q345.269
95-th percentile80.50295
Maximum88.196
Range88.196
Interquartile range (IQR)34.75175

Descriptive statistics

Standard deviation23.584765
Coefficient of variation (CV)0.67162199
Kurtosis-0.7348944
Mean35.11613
Median Absolute Deviation (MAD)23.0715
Skewness0.17259987
Sum3511.613
Variance556.24115
MonotonicityNot monotonic
2023-12-10T19:49:55.447569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
80.488 1
 
1.0%
10.053 1
 
1.0%
31.423 1
 
1.0%
35.173 1
 
1.0%
43.226 1
 
1.0%
36.9 1
 
1.0%
36.02 1
 
1.0%
42.987 1
 
1.0%
29.294 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.381 1
 
1.0%
0.5 1
 
1.0%
8.809 1
 
1.0%
9.69 1
 
1.0%
9.727 1
 
1.0%
9.784 1
 
1.0%
9.867 1
 
1.0%
9.887 1
 
1.0%
9.933 1
 
1.0%
ValueCountFrequency (%)
88.196 1
1.0%
85.157 1
1.0%
81.325 1
1.0%
81.146 1
1.0%
80.787 1
1.0%
80.488 1
1.0%
68.466 1
1.0%
66.575 1
1.0%
64.492 1
1.0%
64.456 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.56646
Minimum0
Maximum55.916
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:55.645177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.82575
median8.864
Q341.891
95-th percentile54.4353
Maximum55.916
Range55.916
Interquartile range (IQR)41.06525

Descriptive statistics

Standard deviation21.984904
Coefficient of variation (CV)1.0689688
Kurtosis-1.4051693
Mean20.56646
Median Absolute Deviation (MAD)8.121
Skewness0.57841095
Sum2056.646
Variance483.33602
MonotonicityNot monotonic
2023-12-10T19:49:55.830073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
8.864 8
 
8.0%
2.052 7
 
7.0%
0.745 4
 
4.0%
8.819 3
 
3.0%
2.047 3
 
3.0%
2.057 3
 
3.0%
33.269 3
 
3.0%
53.645 2
 
2.0%
15.364 2
 
2.0%
Other values (48) 56
56.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.737 1
 
1.0%
0.741 1
 
1.0%
0.745 4
4.0%
0.749 1
 
1.0%
0.754 2
 
2.0%
0.758 1
 
1.0%
0.762 2
 
2.0%
0.779 1
 
1.0%
0.788 1
 
1.0%
ValueCountFrequency (%)
55.916 2
2.0%
54.748 1
1.0%
54.642 1
1.0%
54.536 1
1.0%
54.43 2
2.0%
54.218 1
1.0%
54.112 1
1.0%
54.006 1
1.0%
53.9 1
1.0%
53.794 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.046
Minimum0
Maximum101.7
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:56.038917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median89
Q396.75
95-th percentile101.6
Maximum101.7
Range101.7
Interquartile range (IQR)96.65

Descriptive statistics

Standard deviation45.053105
Coefficient of variation (CV)0.80385943
Kurtosis-1.8575962
Mean56.046
Median Absolute Deviation (MAD)12.65
Skewness-0.26891561
Sum5604.6
Variance2029.7823
MonotonicityNot monotonic
2023-12-10T19:49:56.289551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.1 19
19.0%
13.2 13
 
13.0%
0.0 9
 
9.0%
101.6 8
 
8.0%
101.0 3
 
3.0%
95.9 3
 
3.0%
92.9 2
 
2.0%
63.6 2
 
2.0%
93.1 2
 
2.0%
96.5 2
 
2.0%
Other values (33) 37
37.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.1 19
19.0%
13.2 13
13.0%
13.3 1
 
1.0%
62.7 1
 
1.0%
63.6 2
 
2.0%
64.0 1
 
1.0%
64.9 1
 
1.0%
73.4 1
 
1.0%
77.0 1
 
1.0%
ValueCountFrequency (%)
101.7 2
 
2.0%
101.6 8
8.0%
101.5 2
 
2.0%
101.2 2
 
2.0%
101.0 3
 
3.0%
100.7 1
 
1.0%
100.5 1
 
1.0%
100.4 1
 
1.0%
98.9 1
 
1.0%
98.1 1
 
1.0%

Interactions

2023-12-10T19:49:51.121454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.394817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.321460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.353823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.323758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.165772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:50.128432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:51.269953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.530028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.461565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.479604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.462047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.305697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:50.266756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:51.425622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.658762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.700631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.626752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.599687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.452649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:50.425821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:51.581350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.781014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.822567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.758616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.691898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.592633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:50.577920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:51.710714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.907911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.949797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.896958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.800555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.720424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:50.710640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:52.119693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.028642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.088467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.024999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.903279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.854219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:50.855562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:52.252428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.168391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.222940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.169176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.029936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:49.990130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:50.982138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:49:56.788803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.8670.9350.9750.903
일자/시간(t)0.0001.0000.0000.1850.5320.3090.0000.000
저수위(m)1.0000.0001.0000.0840.8630.8070.8970.857
강우량(mm)0.0000.1850.0841.0000.1180.6430.6230.563
유입량(ms)0.8670.5320.8630.1181.0000.9080.6990.750
방류량(ms)0.9350.3090.8070.6430.9081.0000.8440.696
저수량(백만m3)0.9750.0000.8970.6230.6990.8441.0000.875
저수율0.9030.0000.8570.5630.7500.6960.8751.000
2023-12-10T19:49:57.001963image/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)1.0000.013-0.0810.0550.086-0.0240.0240.000
저수위(m)0.0131.0000.0890.1600.0980.1610.3910.989
강우량(mm)-0.0810.0891.0000.4110.3750.3670.3330.000
유입량(ms)0.0550.1600.4111.0000.9350.6590.7930.650
방류량(ms)0.0860.0980.3750.9351.0000.5890.7470.603
저수량(백만m3)-0.0240.1610.3670.6590.5891.0000.7410.742
저수율0.0240.3910.3330.7930.7470.7411.0000.773
댐이름0.0000.9890.0000.6500.6030.7420.7731.000

Missing values

2023-12-10T19:49:52.450341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:49:52.667490image/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
0강천보2021022038.020.080.48880.4888.819101.0
1강천보2021021338.030.063.29263.2928.864101.6
2강천보2021021538.030.658962.68162.6818.864101.6
3강천보2021021038.030.064.45664.4568.864101.6
4강천보2021021238.030.063.9563.958.864101.6
5강천보2021021938.020.071583.58585.1578.819101.0
6강천보2021022437.940.066.93366.5758.54297.9
7강천보2021020838.030.064.28364.2838.864101.6
8강천보2021020238.020.021978.00381.1468.819101.0
9강천보2021021738.010.048563.27864.3268.773100.5
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90달성보2021020813.590.013834.32939.24154.21892.6
91달성보2021020413.570.072545.01341.32954.00692.2
92달성보2021020213.630.039586.92780.78754.64293.3
93달성보2021021113.640.055.44840.71254.74893.5
94달성보2021022213.750.000662.40939.07755.91695.5
95백제보202102062.840.041.82243.66215.15262.7
96백제보202102242.90.039.91241.13915.4764.0
97백제보202102122.880.040.87140.87115.36463.6
98백제보202102222.940.039.7239.7215.68264.9
99백제보202102112.880.041.29641.29615.36463.6