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 유입량(ms) and 2 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) 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 2 other fieldsHigh correlation
강우량(mm) has 56 (56.0%) 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 10:53:26.438187
Analysis finished2023-12-10 10:53:34.798669
Duration8.36 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

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

2023-12-10T19:53:34.926968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:35.094858image/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%
Mean20191015
Minimum20191001
Maximum20191031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:35.256437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191001
5-th percentile20191002
Q120191007
median20191015
Q320191023
95-th percentile20191030
Maximum20191031
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5635675 × 10-7
Kurtosis-1.2602055
Mean20191015
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0191015 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T19:53:35.476370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20191001 4
 
4.0%
20191003 4
 
4.0%
20191004 4
 
4.0%
20191005 4
 
4.0%
20191006 4
 
4.0%
20191007 4
 
4.0%
20191002 4
 
4.0%
20191026 3
 
3.0%
20191022 3
 
3.0%
20191023 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20191001 4
4.0%
20191002 4
4.0%
20191003 4
4.0%
20191004 4
4.0%
20191005 4
4.0%
20191006 4
4.0%
20191007 4
4.0%
20191008 3
3.0%
20191009 3
3.0%
20191010 3
3.0%
ValueCountFrequency (%)
20191031 3
3.0%
20191030 3
3.0%
20191029 3
3.0%
20191028 3
3.0%
20191027 3
3.0%
20191026 3
3.0%
20191025 3
3.0%
20191024 3
3.0%
20191023 3
3.0%
20191022 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9438
Minimum3.68
Maximum62.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:35.695444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.68
5-th percentile3.719
Q17.4175
median38.03
Q361.1325
95-th percentile62.472
Maximum62.77
Range59.09
Interquartile range (IQR)53.715

Descriptive statistics

Standard deviation22.514906
Coefficient of variation (CV)0.64431761
Kurtosis-1.3933018
Mean34.9438
Median Absolute Deviation (MAD)24.17
Skewness-0.17913662
Sum3494.38
Variance506.92097
MonotonicityNot monotonic
2023-12-10T19:53:35.917773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.02 8
 
8.0%
38.03 7
 
7.0%
62.34 4
 
4.0%
62.35 4
 
4.0%
3.69 3
 
3.0%
4.32 3
 
3.0%
61.07 2
 
2.0%
62.2 2
 
2.0%
62.51 2
 
2.0%
38.1 2
 
2.0%
Other values (56) 63
63.0%
ValueCountFrequency (%)
3.68 1
 
1.0%
3.69 3
3.0%
3.7 1
 
1.0%
3.72 1
 
1.0%
3.77 1
 
1.0%
4.15 1
 
1.0%
4.21 1
 
1.0%
4.31 1
 
1.0%
4.32 3
3.0%
4.33 1
 
1.0%
ValueCountFrequency (%)
62.77 1
 
1.0%
62.52 2
2.0%
62.51 2
2.0%
62.47 1
 
1.0%
62.35 4
4.0%
62.34 4
4.0%
62.33 1
 
1.0%
62.23 1
 
1.0%
62.21 2
2.0%
62.2 2
2.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.850959
Minimum0
Maximum91.3363
Zeros56
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:36.159640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.044975
95-th percentile20.137555
Maximum91.3363
Range91.3363
Interquartile range (IQR)0.044975

Descriptive statistics

Standard deviation11.898639
Coefficient of variation (CV)4.1735565
Kurtosis34.774102
Mean2.850959
Median Absolute Deviation (MAD)0
Skewness5.5586208
Sum285.0959
Variance141.5776
MonotonicityNot monotonic
2023-12-10T19:53:36.403258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.0 56
56.0%
0.0119 4
 
4.0%
0.0101 1
 
1.0%
0.021 1
 
1.0%
0.0406 1
 
1.0%
0.0447 1
 
1.0%
0.0416 1
 
1.0%
0.0175 1
 
1.0%
0.4368 1
 
1.0%
0.0458 1
 
1.0%
Other values (32) 32
32.0%
ValueCountFrequency (%)
0.0 56
56.0%
0.0062 1
 
1.0%
0.0101 1
 
1.0%
0.0119 4
 
4.0%
0.0174 1
 
1.0%
0.0175 1
 
1.0%
0.018 1
 
1.0%
0.021 1
 
1.0%
0.0272 1
 
1.0%
0.0317 1
 
1.0%
ValueCountFrequency (%)
91.3363 1
1.0%
53.2008 1
1.0%
41.1994 1
1.0%
31.0095 1
1.0%
20.9043 1
1.0%
20.0972 1
1.0%
8.8628 1
1.0%
6.8848 1
1.0%
4.9397 1
1.0%
1.7417 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.36432
Minimum0
Maximum3422.727
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:36.645506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median78.4765
Q3125.552
95-th percentile376.8548
Maximum3422.727
Range3422.727
Interquartile range (IQR)125.552

Descriptive statistics

Standard deviation376.38859
Coefficient of variation (CV)2.4703197
Kurtosis59.21185
Mean152.36432
Median Absolute Deviation (MAD)74.5125
Skewness7.1282987
Sum15236.432
Variance141668.37
MonotonicityNot monotonic
2023-12-10T19:53:36.891990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
244.58 1
 
1.0%
56.448 1
 
1.0%
65.438 1
 
1.0%
66.099 1
 
1.0%
64.565 1
 
1.0%
65.968 1
 
1.0%
59.501 1
 
1.0%
66.394 1
 
1.0%
110.476 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
40.65 1
 
1.0%
41.072 1
 
1.0%
41.281 1
 
1.0%
41.718 1
 
1.0%
41.801 1
 
1.0%
43.372 1
 
1.0%
43.945 1
 
1.0%
48.654 1
 
1.0%
54.973 1
 
1.0%
ValueCountFrequency (%)
3422.727 1
1.0%
1326.977 1
1.0%
770.436 1
1.0%
560.622 1
1.0%
531.834 1
1.0%
368.698 1
1.0%
348.986 1
1.0%
346.462 1
1.0%
335.474 1
1.0%
326.878 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.21252
Minimum0
Maximum3435.54
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:37.145694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median78.7385
Q3129.23075
95-th percentile374.32495
Maximum3435.54
Range3435.54
Interquartile range (IQR)129.23075

Descriptive statistics

Standard deviation377.254
Coefficient of variation (CV)2.4622922
Kurtosis59.51641
Mean153.21252
Median Absolute Deviation (MAD)75.0365
Skewness7.1485447
Sum15321.252
Variance142320.58
MonotonicityNot monotonic
2023-12-10T19:53:37.702962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
241.585 1
 
1.0%
56.281 1
 
1.0%
65.438 1
 
1.0%
66.099 1
 
1.0%
64.509 1
 
1.0%
66.079 1
 
1.0%
59.891 1
 
1.0%
71.615 1
 
1.0%
110.476 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
40.256 1
 
1.0%
41.072 1
 
1.0%
41.202 1
 
1.0%
41.876 1
 
1.0%
41.959 1
 
1.0%
43.787 1
 
1.0%
56.281 1
 
1.0%
59.891 1
 
1.0%
61.064 1
 
1.0%
ValueCountFrequency (%)
3435.54 1
1.0%
1323.208 1
1.0%
767.198 1
1.0%
567.561 1
1.0%
527.312 1
1.0%
366.273 1
1.0%
350.364 1
1.0%
332.236 1
1.0%
331.4 1
1.0%
330.452 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.08636
Minimum0
Maximum54.035
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:37.931770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.1965
Q38.97675
95-th percentile52.9443
Maximum54.035
Range54.035
Interquartile range (IQR)8.97675

Descriptive statistics

Standard deviation13.133088
Coefficient of variation (CV)1.6241038
Kurtosis7.5901377
Mean8.08636
Median Absolute Deviation (MAD)4.052
Skewness2.8690485
Sum808.636
Variance172.47799
MonotonicityNot monotonic
2023-12-10T19:53:38.158568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 31
31.0%
8.819 8
 
8.0%
8.864 7
 
7.0%
2.076 3
 
3.0%
2.443 3
 
3.0%
10.591 2
 
2.0%
9.135 2
 
2.0%
9.045 2
 
2.0%
53.254 2
 
2.0%
9.181 2
 
2.0%
Other values (38) 38
38.0%
ValueCountFrequency (%)
0.0 31
31.0%
2.071 1
 
1.0%
2.076 3
 
3.0%
2.081 1
 
1.0%
2.09 1
 
1.0%
2.114 1
 
1.0%
2.327 1
 
1.0%
2.368 1
 
1.0%
2.436 1
 
1.0%
2.443 3
 
3.0%
ValueCountFrequency (%)
54.035 1
1.0%
53.84 1
1.0%
53.645 1
1.0%
53.254 2
2.0%
52.928 1
1.0%
52.652 1
1.0%
10.631 1
1.0%
10.629 1
1.0%
10.591 2
2.0%
10.311 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.177
Minimum0
Maximum121.8
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:38.381312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33.45
Q3101
95-th percentile105.275
Maximum121.8
Range121.8
Interquartile range (IQR)101

Descriptive statistics

Standard deviation46.14
Coefficient of variation (CV)0.93824348
Kurtosis-1.797826
Mean49.177
Median Absolute Deviation (MAD)33.45
Skewness0.18344815
Sum4917.7
Variance2128.8996
MonotonicityNot monotonic
2023-12-10T19:53:38.610565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 31
31.0%
101.0 10
 
10.0%
101.6 7
 
7.0%
13.4 4
 
4.0%
15.7 4
 
4.0%
103.6 2
 
2.0%
68.2 2
 
2.0%
102.1 2
 
2.0%
15.8 2
 
2.0%
105.2 2
 
2.0%
Other values (33) 34
34.0%
ValueCountFrequency (%)
0.0 31
31.0%
13.3 1
 
1.0%
13.4 4
 
4.0%
13.5 1
 
1.0%
13.6 1
 
1.0%
15.0 1
 
1.0%
15.2 1
 
1.0%
15.7 4
 
4.0%
15.8 2
 
2.0%
15.9 1
 
1.0%
ValueCountFrequency (%)
121.8 1
1.0%
113.0 1
1.0%
111.9 1
1.0%
110.9 1
1.0%
106.7 1
1.0%
105.2 2
2.0%
104.7 2
2.0%
103.6 2
2.0%
102.6 1
1.0%
102.5 1
1.0%

Interactions

2023-12-10T19:53:33.422750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.830558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.898167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.894754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:29.842069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:31.011192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:32.340250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:33.586408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.956488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.057557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:29.067853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.046612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:31.176153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:32.517640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:33.717303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.087511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.185273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:29.202893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.192501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:31.308857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:32.671089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:33.832588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.260678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.314541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:29.320786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.390145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:31.447432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:32.804382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:33.974962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.438047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.458807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:29.460330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.603314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:31.607559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:32.972362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:34.125942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.591406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.600497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:29.581152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.748173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:32.042625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:33.140265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:34.281710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.733949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.736652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:29.691152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.872215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:32.180350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:33.277833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:53:38.754678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.3750.3760.3290.8440.979
일자/시간(t)0.0001.0000.0000.0000.4020.2450.3600.429
저수위(m)1.0000.0001.0000.3750.3760.3290.8440.979
강우량(mm)0.3750.0000.3751.0000.7680.7400.6540.133
유입량(ms)0.3760.4020.3760.7681.0001.0000.4790.000
방류량(ms)0.3290.2450.3290.7401.0001.0000.4370.000
저수량(백만m3)0.8440.3600.8440.6540.4790.4371.0000.790
저수율0.9790.4290.9790.1330.0000.0000.7901.000
2023-12-10T19:53:38.944317image/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)1.000-0.048-0.370-0.390-0.392-0.377-0.2160.000
저수위(m)-0.0481.000-0.359-0.520-0.520-0.434-0.3281.000
강우량(mm)-0.370-0.3591.0000.6270.6300.6750.5870.246
유입량(ms)-0.390-0.5200.6271.0000.9990.9110.8220.312
방류량(ms)-0.392-0.5200.6300.9991.0000.9120.8220.271
저수량(백만m3)-0.377-0.4340.6750.9110.9121.0000.9150.910
저수율-0.216-0.3280.5870.8220.8220.9151.0000.790
댐이름0.0001.0000.2460.3120.2710.9100.7901.000

Missing values

2023-12-10T19:53:34.482564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:53:34.692350image/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
0강천보2019100138.020.0101110.476110.4768.819101.0
1강천보2019100238.0741.1994116.818114.1999.045103.6
2강천보2019100338.4220.9043368.698350.36410.629121.8
3강천보2019100438.090.0814348.986366.2739.135104.7
4강천보2019100538.250.3049177.962169.5819.86113.0
5강천보2019100638.230.0149.025150.0739.769111.9
6강천보2019100738.216.8848145.768146.8169.678110.9
7강천보2019100838.10.1868130.061135.8239.181105.2
8강천보2019100938.10.0776.50476.5049.181105.2
9강천보2019101038.090.011980.44980.9739.135104.7
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구담보2019102962.340.00.00.00.00.0
91구담보2019103062.330.00.00.00.00.0
92구담보2019103162.340.00.00.00.00.0
93구미보2019100132.490.0703130.783137.74852.65299.9
94구미보2019100232.791.3363346.462330.45254.035102.5
95구미보2019100332.5331.00953422.7273435.5452.928100.4
96구미보2019100432.580.12791326.9771323.20853.254101.0
97구미보2019100532.640.5731531.834527.31253.645101.7
98구미보2019100632.580.0326.878331.453.254101.0
99구미보2019100732.674.9397265.989259.20653.84102.1