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
강우량(mm) is highly overall correlated with 방류량(ms) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수위(m) is highly imbalanced (91.9%)Imbalance
유입량(ms) is highly imbalanced (63.7%)Imbalance
일자/시간(t) has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:29:53.186192
Analysis finished2023-12-10 10:29:59.010633
Duration5.82 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

2023-12-10T19:29:59.126656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:29:59.275904image/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%
Mean2.0190624 × 109
Minimum2.0190616 × 109
Maximum2.019063 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:29:59.468047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190616 × 109
5-th percentile2.0190616 × 109
Q12.0190621 × 109
median2.0190623 × 109
Q32.0190628 × 109
95-th percentile2.019063 × 109
Maximum2.019063 × 109
Range1422
Interquartile range (IQR)711

Descriptive statistics

Standard deviation454.16765
Coefficient of variation (CV)2.2493988 × 10-7
Kurtosis-1.1034039
Mean2.0190624 × 109
Median Absolute Deviation (MAD)408
Skewness-0.21429785
Sum2.0190624 × 1011
Variance206268.25
MonotonicityNot monotonic
2023-12-10T19:29:59.739823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019062921 1
 
1.0%
2019062403 1
 
1.0%
2019062213 1
 
1.0%
2019062219 1
 
1.0%
2019062221 1
 
1.0%
2019062301 1
 
1.0%
2019062303 1
 
1.0%
2019062311 1
 
1.0%
2019062313 1
 
1.0%
2019062317 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019061601 1
1.0%
2019061603 1
1.0%
2019061605 1
1.0%
2019061607 1
1.0%
2019061609 1
1.0%
2019061611 1
1.0%
2019061613 1
1.0%
2019061615 1
1.0%
2019061617 1
1.0%
2019061619 1
1.0%
ValueCountFrequency (%)
2019063023 1
1.0%
2019063021 1
1.0%
2019063019 1
1.0%
2019063017 1
1.0%
2019063015 1
1.0%
2019063013 1
1.0%
2019063011 1
1.0%
2019063009 1
1.0%
2019063007 1
1.0%
2019063005 1
1.0%

저수위(m)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
99 
8
 
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%
8 1
 
1.0%

Length

2023-12-10T19:30:00.006460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:30:00.203053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 99
99.0%
8 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.14079
Minimum1.103
Maximum1.201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:00.449863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.103
5-th percentile1.109
Q11.123
median1.137
Q31.15275
95-th percentile1.1807
Maximum1.201
Range0.098
Interquartile range (IQR)0.02975

Descriptive statistics

Standard deviation0.024322868
Coefficient of variation (CV)0.021321074
Kurtosis-0.10610842
Mean1.14079
Median Absolute Deviation (MAD)0.014
Skewness0.58222801
Sum114.079
Variance0.00059160192
MonotonicityNot monotonic
2023-12-10T19:30:00.723917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1.151 13
13.0%
1.109 12
12.0%
1.144 11
11.0%
1.123 10
10.0%
1.137 10
10.0%
1.116 9
9.0%
1.13 8
8.0%
1.158 8
8.0%
1.165 4
 
4.0%
1.173 4
 
4.0%
Other values (4) 11
11.0%
ValueCountFrequency (%)
1.103 2
 
2.0%
1.109 12
12.0%
1.116 9
9.0%
1.123 10
10.0%
1.13 8
8.0%
1.137 10
10.0%
1.144 11
11.0%
1.151 13
13.0%
1.158 8
8.0%
1.165 4
 
4.0%
ValueCountFrequency (%)
1.201 4
 
4.0%
1.194 1
 
1.0%
1.18 4
 
4.0%
1.173 4
 
4.0%
1.165 4
 
4.0%
1.158 8
8.0%
1.151 13
13.0%
1.144 11
11.0%
1.137 10
10.0%
1.13 8
8.0%

유입량(ms)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1.6
89 
1.7
9 
1.5
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1.6 89
89.0%
1.7 9
 
9.0%
1.5 2
 
2.0%

Length

2023-12-10T19:30:00.939931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:30:01.125342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.6 89
89.0%
1.7 9
 
9.0%
1.5 2
 
2.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.14804
Minimum6.2
Maximum24.629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:01.300965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile10.0397
Q112.58925
median14.506
Q316.975
95-th percentile23.8
Maximum24.629
Range18.429
Interquartile range (IQR)4.38575

Descriptive statistics

Standard deviation3.9218683
Coefficient of variation (CV)0.25890269
Kurtosis0.0091535133
Mean15.14804
Median Absolute Deviation (MAD)2.294
Skewness0.63965921
Sum1514.804
Variance15.381051
MonotonicityNot monotonic
2023-12-10T19:30:01.565745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.6 9
 
9.0%
11.2 8
 
8.0%
16.8 6
 
6.0%
14.0 5
 
5.0%
15.4 5
 
5.0%
11.9 5
 
5.0%
23.8 4
 
4.0%
14.7 3
 
3.0%
18.9 2
 
2.0%
19.6 2
 
2.0%
Other values (49) 51
51.0%
ValueCountFrequency (%)
6.2 1
 
1.0%
8.412 1
 
1.0%
8.606 1
 
1.0%
9.541 1
 
1.0%
9.559 1
 
1.0%
10.065 1
 
1.0%
10.5 1
 
1.0%
11.2 8
8.0%
11.9 5
5.0%
12.056 1
 
1.0%
ValueCountFrequency (%)
24.629 1
 
1.0%
24.062 1
 
1.0%
23.8 4
4.0%
23.1 1
 
1.0%
22.697 1
 
1.0%
21.186 1
 
1.0%
21.0 2
2.0%
20.685 1
 
1.0%
20.242 1
 
1.0%
19.6 2
2.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.01187
Minimum5.597
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:01.836250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.597
5-th percentile10.63415
Q112.2125
median14.8635
Q316.829
95-th percentile21.105
Maximum23.8
Range18.203
Interquartile range (IQR)4.6165

Descriptive statistics

Standard deviation3.5539396
Coefficient of variation (CV)0.23674197
Kurtosis0.47590825
Mean15.01187
Median Absolute Deviation (MAD)2.2635
Skewness0.3932122
Sum1501.187
Variance12.630487
MonotonicityNot monotonic
2023-12-10T19:30:02.075355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.6 9
 
9.0%
11.2 8
 
8.0%
14.0 6
 
6.0%
16.8 6
 
6.0%
15.4 5
 
5.0%
11.9 5
 
5.0%
23.8 4
 
4.0%
14.7 3
 
3.0%
18.27 2
 
2.0%
17.5 2
 
2.0%
Other values (47) 50
50.0%
ValueCountFrequency (%)
5.597 1
 
1.0%
6.2 1
 
1.0%
10.5 1
 
1.0%
10.512 1
 
1.0%
10.523 1
 
1.0%
10.64 1
 
1.0%
11.2 8
8.0%
11.223 1
 
1.0%
11.375 1
 
1.0%
11.503 1
 
1.0%
ValueCountFrequency (%)
23.8 4
4.0%
23.1 1
 
1.0%
21.0 1
 
1.0%
20.697 1
 
1.0%
20.16 1
 
1.0%
20.09 1
 
1.0%
19.6 2
2.0%
18.9 2
2.0%
18.783 1
 
1.0%
18.573 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2352
Minimum23.18
Maximum23.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:02.286172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.18
5-th percentile23.19
Q123.21
median23.23
Q323.2525
95-th percentile23.291
Maximum23.32
Range0.14
Interquartile range (IQR)0.0425

Descriptive statistics

Standard deviation0.034450374
Coefficient of variation (CV)0.0014826803
Kurtosis-0.14598668
Mean23.2352
Median Absolute Deviation (MAD)0.02
Skewness0.5503145
Sum2323.52
Variance0.0011868283
MonotonicityNot monotonic
2023-12-10T19:30:02.487525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
23.25 13
13.0%
23.19 12
12.0%
23.24 11
11.0%
23.21 10
10.0%
23.23 10
10.0%
23.2 9
9.0%
23.22 8
8.0%
23.26 8
8.0%
23.27 4
 
4.0%
23.28 4
 
4.0%
Other values (4) 11
11.0%
ValueCountFrequency (%)
23.18 2
 
2.0%
23.19 12
12.0%
23.2 9
9.0%
23.21 10
10.0%
23.22 8
8.0%
23.23 10
10.0%
23.24 11
11.0%
23.25 13
13.0%
23.26 8
8.0%
23.27 4
 
4.0%
ValueCountFrequency (%)
23.32 4
 
4.0%
23.31 1
 
1.0%
23.29 4
 
4.0%
23.28 4
 
4.0%
23.27 4
 
4.0%
23.26 8
8.0%
23.25 13
13.0%
23.24 11
11.0%
23.23 10
10.0%
23.22 8
8.0%

Interactions

2023-12-10T19:29:57.601998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:54.299399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:55.228908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:55.962964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:56.833239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:57.755592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:54.538013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:55.384108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:56.122549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:56.987655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:57.894585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:54.733121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:55.517851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:56.261524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:57.128274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:58.132389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:54.894389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:55.665848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:56.487745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:57.276635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:58.400970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:55.049638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:55.811332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:56.664512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:29:57.428992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:30:02.668588image/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)1.0000.0790.2960.4270.4800.4220.337
저수위(m)0.0791.0000.0000.0000.0000.1090.000
강우량(mm)0.2960.0001.0000.8920.7880.9500.999
유입량(ms)0.4270.0000.8921.0000.7650.9610.892
방류량(ms)0.4800.0000.7880.7651.0000.8030.804
저수량(백만m3)0.4220.1090.9500.9610.8031.0000.956
저수율0.3370.0000.9990.8920.8040.9561.000
2023-12-10T19:30:03.297222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유입량(ms)저수위(m)
유입량(ms)1.0000.000
저수위(m)0.0001.000
2023-12-10T19:30:03.539786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)방류량(ms)저수량(백만m3)저수율저수위(m)유입량(ms)
일자/시간(t)1.0000.1370.1800.1950.1370.0580.294
강우량(mm)0.1371.0000.8290.8861.0000.0000.605
방류량(ms)0.1800.8291.0000.8680.8290.0000.618
저수량(백만m3)0.1950.8860.8681.0000.8860.1010.741
저수율0.1371.0000.8290.8861.0000.0000.605
저수위(m)0.0580.0000.0000.1010.0001.0000.000
유입량(ms)0.2940.6050.6180.7410.6050.0001.000

Missing values

2023-12-10T19:29:58.674432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:29:58.925920image/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
0군남201906292101.131.614.014.023.22
1군남201906160501.1161.613.83211.88823.2
2군남201906212101.1161.611.911.923.2
3군남201906292301.1231.612.612.623.21
4군남201906280701.1651.620.24218.2723.27
5군남201906160701.1441.621.18615.35323.24
6군남201906230501.1511.621.015.16723.25
7군남201906212301.1091.69.55911.50323.19
8군남201906201501.1581.614.83918.78323.26
9군남201906300101.1231.612.612.623.21
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군남201906301501.1941.723.123.123.31
91군남201906301301.181.724.06220.0923.29
92군남201906300901.1161.613.16711.22323.2
93군남201906300701.1091.611.211.223.19
94군남201906291901.1371.612.98914.93323.23
95군남201906291701.1441.614.29616.2423.24
96군남201906291301.1581.616.29818.2723.26
97군남201906291101.1731.618.1620.1623.28
98군남201906160101.1161.611.911.923.2
99군남201906160301.1091.611.211.223.19