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

Categorical5
Numeric3

Alerts

댐이름 has constant value ""Constant
유입량(ms) is highly overall correlated with 일자/시간(t) and 4 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
일자/시간(t) is highly overall correlated with 방류량(ms) and 4 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수위(m) is highly imbalanced (64.4%)Imbalance
일자/시간(t) has unique valuesUnique
방류량(ms) has 12 (12.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:28:35.204693
Analysis finished2023-12-10 13:28:37.225932
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군위
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row군위
2nd row군위
3rd row군위
4th row군위
5th row군위

Common Values

ValueCountFrequency (%)
군위 100
100.0%

Length

2023-12-10T22:28:37.318001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:37.444145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190409 × 1011
Minimum2.0190409 × 1011
Maximum2.0190409 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:37.975749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190409 × 1011
5-th percentile2.0190409 × 1011
Q12.0190409 × 1011
median2.0190409 × 1011
Q32.0190409 × 1011
95-th percentile2.0190409 × 1011
Maximum2.0190409 × 1011
Range1670
Interquartile range (IQR)825

Descriptive statistics

Standard deviation483.80099
Coefficient of variation (CV)2.3961921 × 10-9
Kurtosis-1.1968508
Mean2.0190409 × 1011
Median Absolute Deviation (MAD)410
Skewness-0.0015635452
Sum2.0190409 × 1013
Variance234063.39
MonotonicityNot monotonic
2023-12-10T22:28:38.191057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201904091340 1
 
1.0%
201904092330 1
 
1.0%
201904092020 1
 
1.0%
201904092050 1
 
1.0%
201904092100 1
 
1.0%
201904092120 1
 
1.0%
201904092130 1
 
1.0%
201904092210 1
 
1.0%
201904092220 1
 
1.0%
201904092240 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201904090730 1
1.0%
201904090740 1
1.0%
201904090750 1
1.0%
201904090800 1
1.0%
201904090810 1
1.0%
201904090820 1
1.0%
201904090830 1
1.0%
201904090840 1
1.0%
201904090850 1
1.0%
201904090900 1
1.0%
ValueCountFrequency (%)
201904092400 1
1.0%
201904092350 1
1.0%
201904092340 1
1.0%
201904092330 1
1.0%
201904092320 1
1.0%
201904092310 1
1.0%
201904092300 1
1.0%
201904092250 1
1.0%
201904092240 1
1.0%
201904092230 1
1.0%

저수위(m)
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
86 
1
 
6
2
 
5
4
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86
86.0%
1 6
 
6.0%
2 5
 
5.0%
4 2
 
2.0%
3 1
 
1.0%

Length

2023-12-10T22:28:38.411163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:38.567816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 86
86.0%
1 6
 
6.0%
2 5
 
5.0%
4 2
 
2.0%
3 1
 
1.0%

강우량(mm)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
22.21
45 
22.246
27 
22.228
18 
22.263
10 

Length

Max length6
Median length6
Mean length5.55
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22.228
2nd row22.263
3rd row22.21
4th row22.228
5th row22.246

Common Values

ValueCountFrequency (%)
22.21 45
45.0%
22.246 27
27.0%
22.228 18
 
18.0%
22.263 10
 
10.0%

Length

2023-12-10T22:28:38.772282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:38.950785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22.21 45
45.0%
22.246 27
27.0%
22.228 18
 
18.0%
22.263 10
 
10.0%

유입량(ms)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
45.6
63 
45.7
37 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45.6
2nd row45.7
3rd row45.6
4th row45.6
5th row45.7

Common Values

ValueCountFrequency (%)
45.6 63
63.0%
45.7 37
37.0%

Length

2023-12-10T22:28:39.158361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:39.281111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45.6 63
63.0%
45.7 37
37.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.27268
Minimum0
Maximum11.052
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:39.430045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.103
median1.107
Q31.11
95-th percentile1.11305
Maximum11.052
Range11.052
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation1.7656968
Coefficient of variation (CV)1.3873847
Kurtosis27.233214
Mean1.27268
Median Absolute Deviation (MAD)0.004
Skewness5.2057898
Sum127.268
Variance3.1176852
MonotonicityNot monotonic
2023-12-10T22:28:39.637390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 12
12.0%
1.108 9
 
9.0%
1.104 9
 
9.0%
1.107 9
 
9.0%
1.112 9
 
9.0%
1.109 7
 
7.0%
1.111 6
 
6.0%
1.102 5
 
5.0%
1.101 5
 
5.0%
1.11 5
 
5.0%
Other values (9) 24
24.0%
ValueCountFrequency (%)
0.0 12
12.0%
1.1 2
 
2.0%
1.101 5
5.0%
1.102 5
5.0%
1.103 5
5.0%
1.104 9
9.0%
1.105 4
 
4.0%
1.106 4
 
4.0%
1.107 9
9.0%
1.108 9
9.0%
ValueCountFrequency (%)
11.052 2
 
2.0%
11.051 1
 
1.0%
1.115 1
 
1.0%
1.114 1
 
1.0%
1.113 4
4.0%
1.112 9
9.0%
1.111 6
6.0%
1.11 5
5.0%
1.109 7
7.0%
1.108 9
9.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.10735
Minimum1.098
Maximum1.121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:39.841614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.098
5-th percentile1.1
Q11.104
median1.108
Q31.111
95-th percentile1.114
Maximum1.121
Range0.023
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.0044548804
Coefficient of variation (CV)0.0040230103
Kurtosis-0.083584267
Mean1.10735
Median Absolute Deviation (MAD)0.003
Skewness0.053362382
Sum110.735
Variance1.984596 × 10-5
MonotonicityNot monotonic
2023-12-10T22:28:40.030658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.108 12
12.0%
1.105 9
 
9.0%
1.109 8
 
8.0%
1.111 8
 
8.0%
1.103 8
 
8.0%
1.106 7
 
7.0%
1.104 7
 
7.0%
1.11 7
 
7.0%
1.113 6
 
6.0%
1.112 5
 
5.0%
Other values (9) 23
23.0%
ValueCountFrequency (%)
1.098 4
 
4.0%
1.1 2
 
2.0%
1.101 3
 
3.0%
1.102 4
 
4.0%
1.103 8
8.0%
1.104 7
7.0%
1.105 9
9.0%
1.106 7
7.0%
1.107 3
 
3.0%
1.108 12
12.0%
ValueCountFrequency (%)
1.121 1
 
1.0%
1.116 1
 
1.0%
1.115 1
 
1.0%
1.114 4
 
4.0%
1.113 6
6.0%
1.112 5
5.0%
1.111 8
8.0%
1.11 7
7.0%
1.109 8
8.0%
1.108 12
12.0%

저수율
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
192.29
45 
192.31
27 
192.3
18 
192.32
10 

Length

Max length6
Median length6
Mean length5.82
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row192.3
2nd row192.32
3rd row192.29
4th row192.3
5th row192.31

Common Values

ValueCountFrequency (%)
192.29 45
45.0%
192.31 27
27.0%
192.3 18
 
18.0%
192.32 10
 
10.0%

Length

2023-12-10T22:28:40.238155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:40.400609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
192.29 45
45.0%
192.31 27
27.0%
192.3 18
 
18.0%
192.32 10
 
10.0%

Interactions

2023-12-10T22:28:36.513407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:35.666067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:36.099217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:36.657810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:35.813633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:36.245770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:36.780165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:35.960962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:36.370312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:28:40.545044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.3530.9430.9860.6640.5680.943
저수위(m)0.3531.0000.2020.1970.0490.3200.202
강우량(mm)0.9430.2021.0001.0000.4230.6341.000
유입량(ms)0.9860.1971.0001.0000.3340.6191.000
방류량(ms)0.6640.0490.4230.3341.0000.6730.423
저수량(백만m3)0.5680.3200.6340.6190.6731.0000.634
저수율0.9430.2021.0001.0000.4230.6341.000
2023-12-10T22:28:40.776133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유입량(ms)저수위(m)강우량(mm)저수율
유입량(ms)1.0000.2360.9900.990
저수위(m)0.2361.0000.1630.163
강우량(mm)0.9900.1631.0001.000
저수율0.9900.1631.0001.000
2023-12-10T22:28:41.026466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)방류량(ms)저수량(백만m3)저수위(m)강우량(mm)유입량(ms)저수율
일자/시간(t)1.000-0.603-0.7670.1510.8360.8600.836
방류량(ms)-0.6031.0000.5950.0300.4130.5320.413
저수량(백만m3)-0.7670.5951.0000.1850.4510.6020.451
저수위(m)0.1510.0300.1851.0000.1630.2360.163
강우량(mm)0.8360.4130.4510.1631.0000.9901.000
유입량(ms)0.8600.5320.6020.2360.9901.0000.990
저수율0.8360.4130.4510.1631.0000.9901.000

Missing values

2023-12-10T22:28:36.943725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:28:37.151965image/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
0군위201904091340022.22845.61.1081.104192.3
1군위201904090750022.26345.71.1131.121192.32
2군위201904091900122.2145.61.1091.109192.29
3군위201904091350022.22845.61.1081.11192.3
4군위201904091030022.24645.71.1091.109192.31
5군위201904090800022.26345.71.1131.113192.32
6군위201904092140022.2145.61.1031.103192.29
7군위201904091910022.2145.61.1081.108192.29
8군위201904091630022.22845.61.1081.108192.3
9군위201904091400022.22845.61.1071.108192.3
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군위201904091510022.22845.61.1091.112192.3
91군위201904091500022.22845.60.01.106192.3
92군위201904091440022.22845.60.01.104192.3
93군위201904091430022.24645.711.0521.108192.31
94군위201904091330022.22845.60.01.111192.3
95군위201904091320022.22845.60.01.111192.3
96군위201904091300022.24645.71.1091.108192.31
97군위201904091250022.24645.71.1091.108192.31
98군위201904090730022.26345.71.1141.114192.32
99군위201904090740022.26345.71.1091.107192.32