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

댐이름 has constant value ""Constant
저수위(m) has constant value ""Constant
일자/시간(t) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수율(ms) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
유입량(백만m3) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
방류량(ms) has 9 (9.0%) zerosZeros

Reproduction

Analysis started2024-04-17 06:02:31.406343
Analysis finished2024-04-17 06:02:34.550032
Duration3.14 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
감포
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
감포 100
100.0%

Length

2024-04-17T15:02:34.604595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:02:34.685330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190609 × 109
Minimum2.0190601 × 109
Maximum2.0190622 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:34.770890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190601 × 109
5-th percentile2.0190601 × 109
Q12.0190603 × 109
median2.0190605 × 109
Q32.019062 × 109
95-th percentile2.0190621 × 109
Maximum2.0190622 × 109
Range2101
Interquartile range (IQR)1698

Descriptive statistics

Standard deviation807.92197
Coefficient of variation (CV)4.001474 × 10-7
Kurtosis-1.3932866
Mean2.0190609 × 109
Median Absolute Deviation (MAD)292
Skewness0.70360805
Sum2.0190609 × 1011
Variance652737.91
MonotonicityNot monotonic
2024-04-17T15:02:34.893378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019060112 1
 
1.0%
2019060610 1
 
1.0%
2019060420 1
 
1.0%
2019060502 1
 
1.0%
2019060504 1
 
1.0%
2019060508 1
 
1.0%
2019060510 1
 
1.0%
2019060518 1
 
1.0%
2019060520 1
 
1.0%
2019060524 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019060102 1
1.0%
2019060104 1
1.0%
2019060106 1
1.0%
2019060108 1
1.0%
2019060110 1
1.0%
2019060112 1
1.0%
2019060114 1
1.0%
2019060116 1
1.0%
2019060118 1
1.0%
2019060120 1
1.0%
ValueCountFrequency (%)
2019062203 1
1.0%
2019062201 1
1.0%
2019062123 1
1.0%
2019062121 1
1.0%
2019062119 1
1.0%
2019062117 1
1.0%
2019062115 1
1.0%
2019062113 1
1.0%
2019062111 1
1.0%
2019062109 1
1.0%

저수위(m)
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-04-17T15:02:34.998641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:02:35.076721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9637
Minimum1.937
Maximum1.983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:35.160773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.937
5-th percentile1.939
Q11.944
median1.97
Q31.977
95-th percentile1.981
Maximum1.983
Range0.046
Interquartile range (IQR)0.033

Descriptive statistics

Standard deviation0.015627934
Coefficient of variation (CV)0.0079584122
Kurtosis-1.3573255
Mean1.9637
Median Absolute Deviation (MAD)0.008
Skewness-0.5542706
Sum196.37
Variance0.00024423232
MonotonicityNot monotonic
2024-04-17T15:02:35.519573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1.977 8
 
8.0%
1.97 8
 
8.0%
1.981 7
 
7.0%
1.979 7
 
7.0%
1.967 7
 
7.0%
1.941 7
 
7.0%
1.944 7
 
7.0%
1.942 7
 
7.0%
1.974 7
 
7.0%
1.972 5
 
5.0%
Other values (8) 30
30.0%
ValueCountFrequency (%)
1.937 3
3.0%
1.939 3
3.0%
1.941 7
7.0%
1.942 7
7.0%
1.944 7
7.0%
1.946 5
5.0%
1.963 1
 
1.0%
1.965 4
4.0%
1.967 7
7.0%
1.969 5
5.0%
ValueCountFrequency (%)
1.983 4
4.0%
1.981 7
7.0%
1.979 7
7.0%
1.977 8
8.0%
1.976 5
5.0%
1.974 7
7.0%
1.972 5
5.0%
1.97 8
8.0%
1.969 5
5.0%
1.967 7
7.0%

저수량
Real number (ℝ)

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03379
Minimum0.02
Maximum0.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:35.609854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.03
Q10.03
median0.03
Q30.03
95-th percentile0.07
Maximum0.08
Range0.06
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.011771645
Coefficient of variation (CV)0.34837658
Kurtosis7.4474157
Mean0.03379
Median Absolute Deviation (MAD)0
Skewness2.8930053
Sum3.379
Variance0.00013857162
MonotonicityNot monotonic
2024-04-17T15:02:35.718457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.03 81
81.0%
0.07 3
 
3.0%
0.031 2
 
2.0%
0.08 2
 
2.0%
0.02 2
 
2.0%
0.028 1
 
1.0%
0.041 1
 
1.0%
0.062 1
 
1.0%
0.04 1
 
1.0%
0.037 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0.02 2
 
2.0%
0.028 1
 
1.0%
0.03 81
81.0%
0.031 2
 
2.0%
0.037 1
 
1.0%
0.038 1
 
1.0%
0.039 1
 
1.0%
0.04 1
 
1.0%
0.041 1
 
1.0%
0.053 1
 
1.0%
ValueCountFrequency (%)
0.08 2
2.0%
0.075 1
 
1.0%
0.07 3
3.0%
0.064 1
 
1.0%
0.062 1
 
1.0%
0.053 1
 
1.0%
0.041 1
 
1.0%
0.04 1
 
1.0%
0.039 1
 
1.0%
0.038 1
 
1.0%

저수율(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.7419
Minimum37.59
Maximum37.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:35.820987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.59
5-th percentile37.6
Q137.63
median37.78
Q337.82
95-th percentile37.84
Maximum37.85
Range0.26
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.089258392
Coefficient of variation (CV)0.0023649682
Kurtosis-1.3577999
Mean37.7419
Median Absolute Deviation (MAD)0.045
Skewness-0.55894358
Sum3774.19
Variance0.0079670606
MonotonicityNot monotonic
2024-04-17T15:02:35.926441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
37.82 8
 
8.0%
37.78 8
 
8.0%
37.84 7
 
7.0%
37.83 7
 
7.0%
37.76 7
 
7.0%
37.61 7
 
7.0%
37.63 7
 
7.0%
37.62 7
 
7.0%
37.8 7
 
7.0%
37.79 5
 
5.0%
Other values (8) 30
30.0%
ValueCountFrequency (%)
37.59 3
3.0%
37.6 3
3.0%
37.61 7
7.0%
37.62 7
7.0%
37.63 7
7.0%
37.64 5
5.0%
37.74 1
 
1.0%
37.75 4
4.0%
37.76 7
7.0%
37.77 5
5.0%
ValueCountFrequency (%)
37.85 4
4.0%
37.84 7
7.0%
37.83 7
7.0%
37.82 8
8.0%
37.81 5
5.0%
37.8 7
7.0%
37.79 5
5.0%
37.78 8
8.0%
37.77 5
5.0%
37.76 7
7.0%

유입량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.512
Minimum73.5
Maximum75.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:36.030290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73.5
5-th percentile73.6
Q173.8
median74.8
Q375
95-th percentile75.2
Maximum75.2
Range1.7
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.59633222
Coefficient of variation (CV)0.0080031703
Kurtosis-1.3545906
Mean74.512
Median Absolute Deviation (MAD)0.3
Skewness-0.55883053
Sum7451.2
Variance0.35561212
MonotonicityNot monotonic
2024-04-17T15:02:36.138196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
74.8 13
13.0%
75.0 13
13.0%
73.8 12
12.0%
75.2 11
11.0%
74.6 11
11.0%
73.6 10
10.0%
74.9 7
7.0%
73.7 7
7.0%
75.1 7
7.0%
74.7 5
 
5.0%
Other values (2) 4
 
4.0%
ValueCountFrequency (%)
73.5 3
 
3.0%
73.6 10
10.0%
73.7 7
7.0%
73.8 12
12.0%
74.5 1
 
1.0%
74.6 11
11.0%
74.7 5
 
5.0%
74.8 13
13.0%
74.9 7
7.0%
75.0 13
13.0%
ValueCountFrequency (%)
75.2 11
11.0%
75.1 7
7.0%
75.0 13
13.0%
74.9 7
7.0%
74.8 13
13.0%
74.7 5
 
5.0%
74.6 11
11.0%
74.5 1
 
1.0%
73.8 12
12.0%
73.7 7
7.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02968
Minimum0
Maximum0.075
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:36.234440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median0.03
Q30.03
95-th percentile0.05345
Maximum0.075
Range0.075
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.013119405
Coefficient of variation (CV)0.44202847
Kurtosis4.3608544
Mean0.02968
Median Absolute Deviation (MAD)0
Skewness0.47008955
Sum2.968
Variance0.00017211879
MonotonicityNot monotonic
2024-04-17T15:02:36.330303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.03 76
76.0%
0.0 9
 
9.0%
0.07 3
 
3.0%
0.031 2
 
2.0%
0.02 2
 
2.0%
0.028 1
 
1.0%
0.041 1
 
1.0%
0.062 1
 
1.0%
0.04 1
 
1.0%
0.053 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
0.0 9
 
9.0%
0.02 2
 
2.0%
0.028 1
 
1.0%
0.03 76
76.0%
0.031 2
 
2.0%
0.038 1
 
1.0%
0.039 1
 
1.0%
0.04 1
 
1.0%
0.041 1
 
1.0%
0.053 1
 
1.0%
ValueCountFrequency (%)
0.075 1
 
1.0%
0.07 3
 
3.0%
0.062 1
 
1.0%
0.053 1
 
1.0%
0.041 1
 
1.0%
0.04 1
 
1.0%
0.039 1
 
1.0%
0.038 1
 
1.0%
0.031 2
 
2.0%
0.03 76
76.0%

Interactions

2024-04-17T15:02:33.856061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:31.597026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.011551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.450994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.936986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.383478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.929603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:31.659510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.089339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.527608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.004542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.459974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:34.003697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:31.724591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.148955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.610316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.074174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.534992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:34.088251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:31.792393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.223204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.690133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.147456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.620521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:34.165954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:31.856645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.294643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.773510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.220426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.695158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:34.255262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:31.937656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.369712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:32.856829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.305864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:33.775391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T15:02:36.408173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
일자/시간(t)1.0000.8320.3860.8320.8610.510
강우량(mm)0.8321.0000.3851.0000.9990.204
저수량0.3860.3851.0000.3850.4280.985
저수율(ms)0.8321.0000.3851.0000.9990.204
유입량(백만m3)0.8610.9990.4280.9991.0000.290
방류량(ms)0.5100.2040.9850.2040.2901.000
2024-04-17T15:02:36.508056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
일자/시간(t)1.000-0.998-0.221-0.998-0.995-0.101
강우량(mm)-0.9981.0000.2191.0000.9970.122
저수량-0.2210.2191.0000.2190.2300.493
저수율(ms)-0.9981.0000.2191.0000.9970.122
유입량(백만m3)-0.9950.9970.2300.9971.0000.131
방류량(ms)-0.1010.1220.4930.1220.1311.000

Missing values

2024-04-17T15:02:34.379261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T15:02:34.498678image/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)저수량저수율(ms)유입량(백만m3)방류량(ms)
0감포201906011201.9810.0337.8475.20.03
1감포201906191701.9460.02837.6473.80.028
2감포201906040401.9740.0337.874.90.03
3감포201906011401.9810.0337.8475.20.03
4감포201906210101.9420.0337.6273.70.03
5감포201906191901.9460.0337.6473.80.03
6감포201906051201.9690.0337.7774.70.03
7감포201906040601.9720.0337.7974.80.0
8감포201906022201.9770.0337.8275.00.03
9감포201906011601.9810.04137.8475.20.041
댐이름일자/시간(t)저수위(m)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
90감포201906020601.9790.0337.8375.10.03
91감포201906020401.9790.0337.8375.10.03
92감포201906012401.9790.03937.8375.10.039
93감포201906012201.9810.07537.8475.20.075
94감포201906011001.9810.06437.8475.20.0
95감포201906010801.9830.0737.8575.20.07
96감포201906010401.9830.0337.8575.20.03
97감포201906010201.9830.0337.8575.20.03
98감포201906191301.9460.0237.6473.80.02
99감포201906191501.9460.0237.6473.80.02