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

Categorical3
Numeric5

Alerts

댐이름 has constant value ""Constant
저수위(m) is highly overall correlated with 저수량(백만m3) and 1 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 1 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 1 other fieldsHigh correlation
강우량(mm) is highly imbalanced (91.9%)Imbalance
일자/시간(t) has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:30:56.265121
Analysis finished2023-12-10 10:31:02.518464
Duration6.25 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
군남
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 100
100.0%

Length

2023-12-10T19:31:02.650594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:02.859039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 100
100.0%

일자/시간(t)
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190223 × 109
Minimum2.0190218 × 109
Maximum2.0190227 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:03.106246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190218 × 109
5-th percentile2.0190218 × 109
Q12.019022 × 109
median2.0190222 × 109
Q32.0190225 × 109
95-th percentile2.0190227 × 109
Maximum2.0190227 × 109
Range912
Interquartile range (IQR)509

Descriptive statistics

Standard deviation298.09096
Coefficient of variation (CV)1.4764125 × 10-7
Kurtosis-1.4199519
Mean2.0190223 × 109
Median Absolute Deviation (MAD)293
Skewness0.056272259
Sum2.0190223 × 1011
Variance88858.222
MonotonicityNot monotonic
2023-12-10T19:31:03.356338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019022110 1
 
1.0%
2019022714 1
 
1.0%
2019022524 1
 
1.0%
2019022606 1
 
1.0%
2019022608 1
 
1.0%
2019022612 1
 
1.0%
2019022614 1
 
1.0%
2019022622 1
 
1.0%
2019022624 1
 
1.0%
2019022704 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019021808 1
1.0%
2019021810 1
1.0%
2019021812 1
1.0%
2019021814 1
1.0%
2019021816 1
1.0%
2019021818 1
1.0%
2019021820 1
1.0%
2019021822 1
1.0%
2019021824 1
1.0%
2019021902 1
1.0%
ValueCountFrequency (%)
2019022720 1
1.0%
2019022718 1
1.0%
2019022716 1
1.0%
2019022714 1
1.0%
2019022712 1
1.0%
2019022710 1
1.0%
2019022708 1
1.0%
2019022706 1
1.0%
2019022704 1
1.0%
2019022702 1
1.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.7344
Minimum25.64
Maximum25.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:03.601568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.64
5-th percentile25.6795
Q125.71
median25.73
Q325.76
95-th percentile25.81
Maximum25.86
Range0.22
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.041641083
Coefficient of variation (CV)0.0016181097
Kurtosis0.23126243
Mean25.7344
Median Absolute Deviation (MAD)0.03
Skewness0.43702806
Sum2573.44
Variance0.0017339798
MonotonicityNot monotonic
2023-12-10T19:31:03.836436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
25.72 13
13.0%
25.71 12
12.0%
25.7 10
10.0%
25.75 8
8.0%
25.76 8
8.0%
25.77 8
8.0%
25.73 6
 
6.0%
25.74 6
 
6.0%
25.78 5
 
5.0%
25.69 5
 
5.0%
Other values (11) 19
19.0%
ValueCountFrequency (%)
25.64 1
 
1.0%
25.65 1
 
1.0%
25.66 2
 
2.0%
25.67 1
 
1.0%
25.68 4
 
4.0%
25.69 5
 
5.0%
25.7 10
10.0%
25.71 12
12.0%
25.72 13
13.0%
25.73 6
6.0%
ValueCountFrequency (%)
25.86 1
 
1.0%
25.84 1
 
1.0%
25.82 2
 
2.0%
25.81 2
 
2.0%
25.8 3
 
3.0%
25.79 1
 
1.0%
25.78 5
5.0%
25.77 8
8.0%
25.76 8
8.0%
25.75 8
8.0%

강우량(mm)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
99 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 99
99.0%
3 1
 
1.0%

Length

2023-12-10T19:31:04.077675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:04.263190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 99
99.0%
3 1
 
1.0%

유입량(ms)
Real number (ℝ)

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.20364
Minimum2.461
Maximum31.843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:04.489914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.461
5-th percentile3.978
Q18.3575
median11.2
Q314.818
95-th percentile23.54175
Maximum31.843
Range29.382
Interquartile range (IQR)6.4605

Descriptive statistics

Standard deviation5.899258
Coefficient of variation (CV)0.48340151
Kurtosis1.1751766
Mean12.20364
Median Absolute Deviation (MAD)3
Skewness1.0647849
Sum1220.364
Variance34.801245
MonotonicityNot monotonic
2023-12-10T19:31:04.831884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.2 10
 
10.0%
9.6 6
 
6.0%
8.4 6
 
6.0%
14.0 4
 
4.0%
11.0 4
 
4.0%
7.561 3
 
3.0%
3.978 3
 
3.0%
7.589 2
 
2.0%
12.039 2
 
2.0%
19.0 2
 
2.0%
Other values (53) 58
58.0%
ValueCountFrequency (%)
2.461 1
 
1.0%
3.833 1
 
1.0%
3.922 1
 
1.0%
3.95 1
 
1.0%
3.978 3
3.0%
4.226 1
 
1.0%
4.596 1
 
1.0%
6.072 1
 
1.0%
6.266 1
 
1.0%
6.626 1
 
1.0%
ValueCountFrequency (%)
31.843 1
1.0%
29.565 1
1.0%
27.883 1
1.0%
25.861 1
1.0%
23.594 1
1.0%
23.539 1
1.0%
22.299 1
1.0%
22.033 1
1.0%
21.909 1
1.0%
21.704 1
1.0%

방류량(ms)
Real number (ℝ)

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.20809
Minimum8.2
Maximum24.607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:05.095991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile8.38085
Q19.6
median11.2
Q313.83575
95-th percentile19.35715
Maximum24.607
Range16.407
Interquartile range (IQR)4.23575

Descriptive statistics

Standard deviation3.8515347
Coefficient of variation (CV)0.31549036
Kurtosis2.714028
Mean12.20809
Median Absolute Deviation (MAD)1.6
Skewness1.6557373
Sum1220.809
Variance14.83432
MonotonicityNot monotonic
2023-12-10T19:31:05.346284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
11.2 25
25.0%
9.6 10
 
10.0%
8.4 10
 
10.0%
11.0 8
 
8.0%
14.0 5
 
5.0%
8.2 4
 
4.0%
9.8 3
 
3.0%
16.4 3
 
3.0%
13.8 2
 
2.0%
19.0 2
 
2.0%
Other values (27) 28
28.0%
ValueCountFrequency (%)
8.2 4
 
4.0%
8.207 1
 
1.0%
8.39 1
 
1.0%
8.397 1
 
1.0%
8.4 10
10.0%
8.662 1
 
1.0%
9.6 10
10.0%
9.8 3
 
3.0%
11.0 8
8.0%
11.103 1
 
1.0%
ValueCountFrequency (%)
24.607 1
1.0%
24.6 1
1.0%
24.593 1
1.0%
24.407 1
1.0%
22.343 1
1.0%
19.2 1
1.0%
19.0 2
2.0%
18.66 1
1.0%
16.807 1
1.0%
16.6 2
2.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.62697
Minimum3.505
Maximum3.792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:05.572989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.505
5-th percentile3.55635
Q13.595
median3.621
Q33.66
95-th percentile3.726
Maximum3.792
Range0.287
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation0.054159595
Coefficient of variation (CV)0.014932463
Kurtosis0.26897123
Mean3.62697
Median Absolute Deviation (MAD)0.039
Skewness0.46766192
Sum362.697
Variance0.0029332617
MonotonicityNot monotonic
2023-12-10T19:31:05.804159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.608 13
13.0%
3.595 12
12.0%
3.582 10
10.0%
3.647 8
8.0%
3.66 8
8.0%
3.673 8
8.0%
3.621 6
 
6.0%
3.634 6
 
6.0%
3.686 5
 
5.0%
3.57 5
 
5.0%
Other values (11) 19
19.0%
ValueCountFrequency (%)
3.505 1
 
1.0%
3.518 1
 
1.0%
3.531 2
 
2.0%
3.544 1
 
1.0%
3.557 4
 
4.0%
3.57 5
 
5.0%
3.582 10
10.0%
3.595 12
12.0%
3.608 13
13.0%
3.621 6
6.0%
ValueCountFrequency (%)
3.792 1
 
1.0%
3.765 1
 
1.0%
3.739 2
 
2.0%
3.726 2
 
2.0%
3.713 3
 
3.0%
3.7 1
 
1.0%
3.686 5
5.0%
3.673 8
8.0%
3.66 8
8.0%
3.647 8
8.0%

저수율
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
5.0
45 
5.1
36 
5.2
13 
4.9
 
4
5.3
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.2
2nd row5.0
3rd row5.2
4th row5.2
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 45
45.0%
5.1 36
36.0%
5.2 13
 
13.0%
4.9 4
 
4.0%
5.3 2
 
2.0%

Length

2023-12-10T19:31:06.046080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:06.290529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5.0 45
45.0%
5.1 36
36.0%
5.2 13
 
13.0%
4.9 4
 
4.0%
5.3 2
 
2.0%

Interactions

2023-12-10T19:31:00.424585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:56.725405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.479471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.224418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.106859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:00.947173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:56.853667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.615380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.411322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.259176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:01.248637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.007382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.747352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.554919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.801126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:01.562612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.158011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.892172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.694466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.953926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:01.772139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.309438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.042263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.897061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:00.133245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:31:06.441949image/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)1.0000.5640.0000.2060.7210.5560.613
저수위(m)0.5641.0000.0000.0000.0001.0001.000
강우량(mm)0.0000.0001.0000.0000.0430.0000.000
유입량(ms)0.2060.0000.0001.0000.6760.0000.000
방류량(ms)0.7210.0000.0430.6761.0000.2290.320
저수량(백만m3)0.5561.0000.0000.0000.2291.0001.000
저수율0.6131.0000.0000.0000.3201.0001.000
2023-12-10T19:31:06.757580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량(mm)저수율
강우량(mm)1.0000.000
저수율0.0001.000
2023-12-10T19:31:07.002062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)강우량(mm)저수율
일자/시간(t)1.000-0.107-0.177-0.326-0.1070.0000.396
저수위(m)-0.1071.0000.2730.4321.0000.0000.973
유입량(ms)-0.1770.2731.0000.3300.2730.0000.000
방류량(ms)-0.3260.4320.3301.0000.4320.0300.185
저수량(백만m3)-0.1071.0000.2730.4321.0000.0000.973
강우량(mm)0.0000.0000.0000.0300.0001.0000.000
저수율0.3960.9730.0000.1850.9730.0001.000

Missing values

2023-12-10T19:31:02.128894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:31:02.438166image/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
0군남201902211025.8107.5214.8533.7265.2
1군남201902181225.7209.89.83.6085.0
2군남201902250825.78014.014.03.6865.2
3군남201902211225.82015.06411.3973.7395.2
4군남201902192025.68015.3398.23.5575.0
5군남201902181425.75021.70414.4823.6475.1
6군남201902261625.7208.48.43.6085.0
7군남201902251025.76010.36114.03.665.1
8군남201902222025.708.28.23.5825.0
9군남201902211425.81011.211.23.7265.2
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군남201902220425.72019.019.03.6085.0
91군남201902220225.69018.13911.03.575.0
92군남201902212225.7203.97811.23.6085.0
93군남201902212025.76011.211.23.665.1
94군남201902210825.8409.41816.8073.7655.3
95군남201902210625.86015.09411.43.7925.3
96군남201902210225.75022.03311.23.6475.1
97군남201902202425.7203.97811.23.6085.0
98군남201902180825.73014.014.03.6215.1
99군남201902181025.7209.89.83.6085.0