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

Categorical2
Numeric6

Alerts

저수위(m) is highly overall correlated with 댐이름 and 1 other fieldsHigh 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
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 75 (75.0%) zerosZeros
유입량(ms) has 8 (8.0%) zerosZeros
방류량(ms) has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:21:28.293146
Analysis finished2023-12-10 13:21:35.226711
Duration6.93 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
군남
30 
한탄강
30 
평화의댐
30 
여주저류지
10 

Length

Max length5
Median length4
Mean length3.2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 30
30.0%
한탄강 30
30.0%
평화의댐 30
30.0%
여주저류지 10
 
10.0%

Length

2023-12-10T22:21:35.395112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:35.651731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 30
30.0%
한탄강 30
30.0%
평화의댐 30
30.0%
여주저류지 10
 
10.0%

일자/시간(t)
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190615
Minimum20190601
Maximum20190630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:35.923548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190601
5-th percentile20190602
Q120190608
median20190616
Q320190623
95-th percentile20190629
Maximum20190630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6637
Coefficient of variation (CV)4.2909539 × 10-7
Kurtosis-1.2064089
Mean20190615
Median Absolute Deviation (MAD)7.5
Skewness-0.041003331
Sum2.0190615 × 109
Variance75.059697
MonotonicityNot monotonic
2023-12-10T22:21:36.184300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190617 4
 
4.0%
20190624 4
 
4.0%
20190620 4
 
4.0%
20190625 4
 
4.0%
20190603 4
 
4.0%
20190623 4
 
4.0%
20190602 4
 
4.0%
20190606 4
 
4.0%
20190613 4
 
4.0%
20190619 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20190601 3
3.0%
20190602 4
4.0%
20190603 4
4.0%
20190604 3
3.0%
20190605 3
3.0%
20190606 4
4.0%
20190607 3
3.0%
20190608 3
3.0%
20190609 3
3.0%
20190610 3
3.0%
ValueCountFrequency (%)
20190630 3
3.0%
20190629 3
3.0%
20190628 3
3.0%
20190627 3
3.0%
20190626 3
3.0%
20190625 4
4.0%
20190624 4
4.0%
20190623 4
4.0%
20190622 3
3.0%
20190621 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.8474
Minimum23.17
Maximum163.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:36.612292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.17
5-th percentile23.1895
Q123.21
median47.275
Q3162.71
95-th percentile162.8815
Maximum163.01
Range139.84
Interquartile range (IQR)139.5

Descriptive statistics

Standard deviation59.962604
Coefficient of variation (CV)0.82312621
Kurtosis-1.2678425
Mean72.8474
Median Absolute Deviation (MAD)24.065
Skewness0.80328094
Sum7284.74
Variance3595.5139
MonotonicityNot monotonic
2023-12-10T22:21:36.930770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.21 11
 
11.0%
23.19 7
 
7.0%
47.31 5
 
5.0%
47.34 5
 
5.0%
162.71 4
 
4.0%
47.23 3
 
3.0%
23.2 3
 
3.0%
162.82 3
 
3.0%
23.18 3
 
3.0%
23.22 3
 
3.0%
Other values (41) 53
53.0%
ValueCountFrequency (%)
23.17 2
 
2.0%
23.18 3
 
3.0%
23.19 7
7.0%
23.2 3
 
3.0%
23.21 11
11.0%
23.22 3
 
3.0%
23.32 1
 
1.0%
28.49 1
 
1.0%
28.5 1
 
1.0%
28.51 1
 
1.0%
ValueCountFrequency (%)
163.01 1
1.0%
162.96 1
1.0%
162.93 1
1.0%
162.92 1
1.0%
162.91 1
1.0%
162.88 1
1.0%
162.87 1
1.0%
162.85 1
1.0%
162.84 1
1.0%
162.83 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.678546
Minimum0
Maximum27
Zeros75
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:37.150730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.226425
95-th percentile20.1
Maximum27
Range27
Interquartile range (IQR)0.226425

Descriptive statistics

Standard deviation6.5218043
Coefficient of variation (CV)2.43483
Kurtosis5.6335654
Mean2.678546
Median Absolute Deviation (MAD)0
Skewness2.613784
Sum267.8546
Variance42.533931
MonotonicityNot monotonic
2023-12-10T22:21:37.362751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 75
75.0%
1.0 2
 
2.0%
24.0 2
 
2.0%
20.0 2
 
2.0%
0.9057 1
 
1.0%
22.0 1
 
1.0%
2.0 1
 
1.0%
27.0 1
 
1.0%
4.0 1
 
1.0%
7.0 1
 
1.0%
Other values (13) 13
 
13.0%
ValueCountFrequency (%)
0.0 75
75.0%
0.9057 1
 
1.0%
1.0 2
 
2.0%
1.2594 1
 
1.0%
2.0 1
 
1.0%
2.0127 1
 
1.0%
2.7174 1
 
1.0%
3.4522 1
 
1.0%
3.5 1
 
1.0%
4.0 1
 
1.0%
ValueCountFrequency (%)
27.0 1
1.0%
24.5122 1
1.0%
24.0 2
2.0%
22.0 1
1.0%
20.0 2
2.0%
19.5852 1
1.0%
19.0 1
1.0%
9.0 1
1.0%
8.5854 1
1.0%
8.0 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.21981
Minimum0
Maximum27.639
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:37.588375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.811
median9.6465
Q316.7325
95-th percentile21.92135
Maximum27.639
Range27.639
Interquartile range (IQR)10.9215

Descriptive statistics

Standard deviation7.1023054
Coefficient of variation (CV)0.63301477
Kurtosis-1.0117866
Mean11.21981
Median Absolute Deviation (MAD)4.9705
Skewness0.19521445
Sum1121.981
Variance50.442743
MonotonicityNot monotonic
2023-12-10T22:21:37.803072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
14.159 1
 
1.0%
5.823 1
 
1.0%
15.728 1
 
1.0%
20.391 1
 
1.0%
22.612 1
 
1.0%
15.885 1
 
1.0%
21.193 1
 
1.0%
19.576 1
 
1.0%
16.678 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 8
8.0%
0.023 1
 
1.0%
0.046 1
 
1.0%
3.981 1
 
1.0%
4.671 1
 
1.0%
4.682 1
 
1.0%
4.686 1
 
1.0%
4.687 1
 
1.0%
4.779 1
 
1.0%
4.82 1
 
1.0%
ValueCountFrequency (%)
27.639 1
1.0%
25.29 1
1.0%
24.558 1
1.0%
24.041 1
1.0%
22.612 1
1.0%
21.885 1
1.0%
21.51 1
1.0%
21.193 1
1.0%
20.391 1
1.0%
20.328 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.2051
Minimum0
Maximum27.002
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:38.038888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.9125
median9.687
Q316.7065
95-th percentile21.45255
Maximum27.002
Range27.002
Interquartile range (IQR)10.794

Descriptive statistics

Standard deviation7.0932036
Coefficient of variation (CV)0.6330335
Kurtosis-1.0272663
Mean11.2051
Median Absolute Deviation (MAD)4.937
Skewness0.1906498
Sum1120.51
Variance50.313538
MonotonicityNot monotonic
2023-12-10T22:21:38.261307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
14.321 1
 
1.0%
4.755 1
 
1.0%
23.306 1
 
1.0%
15.885 1
 
1.0%
21.355 1
 
1.0%
19.657 1
 
1.0%
15.868 1
 
1.0%
18.976 1
 
1.0%
27.002 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 10
10.0%
3.819 1
 
1.0%
4.74 1
 
1.0%
4.745 1
 
1.0%
4.755 1
 
1.0%
4.779 1
 
1.0%
4.805 1
 
1.0%
4.813 1
 
1.0%
4.82 1
 
1.0%
4.856 1
 
1.0%
ValueCountFrequency (%)
27.002 1
1.0%
25.264 1
1.0%
24.977 1
1.0%
23.891 1
1.0%
23.306 1
1.0%
21.355 1
1.0%
21.121 1
1.0%
20.827 1
1.0%
20.611 1
1.0%
20.457 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80083
Minimum0.01
Maximum1.201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:38.475356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.017
Q10.508
median1.022
Q31.109
95-th percentile1.13
Maximum1.201
Range1.191
Interquartile range (IQR)0.601

Descriptive statistics

Standard deviation0.37048726
Coefficient of variation (CV)0.4626291
Kurtosis-0.55541264
Mean0.80083
Median Absolute Deviation (MAD)0.101
Skewness-0.84291623
Sum80.083
Variance0.13726081
MonotonicityNot monotonic
2023-12-10T22:21:38.706781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.123 11
 
11.0%
1.109 7
 
7.0%
0.508 5
 
5.0%
0.512 5
 
5.0%
0.975 4
 
4.0%
0.497 3
 
3.0%
0.017 3
 
3.0%
1.045 3
 
3.0%
1.025 3
 
3.0%
0.495 3
 
3.0%
Other values (40) 53
53.0%
ValueCountFrequency (%)
0.01 2
2.0%
0.012 1
 
1.0%
0.014 1
 
1.0%
0.017 3
3.0%
0.019 1
 
1.0%
0.021 1
 
1.0%
0.027 1
 
1.0%
0.489 1
 
1.0%
0.492 1
 
1.0%
0.494 1
 
1.0%
ValueCountFrequency (%)
1.201 1
 
1.0%
1.172 1
 
1.0%
1.138 1
 
1.0%
1.13 3
 
3.0%
1.123 11
11.0%
1.117 1
 
1.0%
1.116 3
 
3.0%
1.111 1
 
1.0%
1.109 7
7.0%
1.104 1
 
1.0%

저수율
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.0
40 
0.2
30 
1.6
24 
1.5
5 
1.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.6
2nd row1.6
3rd row1.6
4th row1.6
5th row1.6

Common Values

ValueCountFrequency (%)
0.0 40
40.0%
0.2 30
30.0%
1.6 24
24.0%
1.5 5
 
5.0%
1.7 1
 
1.0%

Length

2023-12-10T22:21:38.925800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:39.086396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 40
40.0%
0.2 30
30.0%
1.6 24
24.0%
1.5 5
 
5.0%
1.7 1
 
1.0%

Interactions

2023-12-10T22:21:33.384809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.756339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.634540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.629976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.526874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.351439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:33.619725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.921343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.782680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.817519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.675700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.493796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:33.893024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.078290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.936623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.975502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.825294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.698992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.077032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.207398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.146705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.111130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.955121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.845367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.254508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.339073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.291879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.243658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.077776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.965425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.391659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.484895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.435386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.385741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.209596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:33.177093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:39.227159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9550.9470.9260.835
일자/시간(t)0.0001.0000.0000.4050.5370.6640.0000.000
저수위(m)1.0000.0001.0000.0000.8970.8840.8760.843
강우량(mm)0.0000.4050.0001.0000.0000.0000.0000.000
유입량(ms)0.9550.5370.8970.0001.0000.9940.9640.905
방류량(ms)0.9470.6640.8840.0000.9941.0000.9590.893
저수량(백만m3)0.9260.0000.8760.0000.9640.9591.0000.931
저수율0.8350.0000.8430.0000.9050.8930.9311.000
2023-12-10T22:21:39.465814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름저수율
댐이름1.0000.804
저수율0.8041.000
2023-12-10T22:21:39.690881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)댐이름저수율
일자/시간(t)1.0000.0420.0060.0970.1000.0700.0000.000
저수위(m)0.0421.0000.1620.4530.451-0.1690.9950.870
강우량(mm)0.0060.1621.0000.1610.1500.1400.0000.000
유입량(ms)0.0970.4530.1611.0000.9990.7080.8570.582
방류량(ms)0.1000.4510.1500.9991.0000.7040.8410.562
저수량(백만m3)0.070-0.1690.1400.7080.7041.0000.9350.635
댐이름0.0000.9950.0000.8570.8410.9351.0000.804
저수율0.0000.8700.0000.5820.5620.6350.8041.000

Missing values

2023-12-10T22:21:34.590421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:35.131482image/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
0군남2019061723.190.014.15914.3211.1091.6
1군남2019061023.219.010.06410.0641.1231.6
2군남2019061423.190.011.15111.1511.1091.6
3군남2019061523.20.013.3813.2991.1161.6
4군남2019061923.213.513.47613.5571.1231.6
5군남2019062123.197.514.10814.271.1091.6
6군남2019062923.211.016.27616.2761.1231.6
7군남2019061323.190.09.6999.9421.1091.6
8군남2019062423.20.014.76114.681.1161.6
9군남2019062623.210.014.6114.611.1231.6
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90여주저류지2019061728.540.00.00.00.0170.0
91여주저류지2019061328.590.00.00.00.0270.0
92여주저류지2019060628.520.00.0460.00.0140.0
93여주저류지2019060228.50.00.00.00.010.0
94여주저류지2019062528.510.00.00.00.0120.0
95여주저류지2019062428.540.00.00.00.0170.0
96여주저류지2019062328.540.00.00.00.0170.0
97여주저류지2019062028.560.00.0230.00.0210.0
98여주저류지2019060328.490.00.00.00.010.0
99여주저류지2019061928.550.00.00.00.0190.0