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) has constant value ""Constant
유입량(ms) has constant value ""Constant
강우량(mm) is highly overall correlated with 방류량(ms) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:15:30.562335
Analysis finished2023-12-10 13:15:35.569812
Duration5.01 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

2023-12-10T22:15:35.727062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:15:35.875177image/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%
Mean2.0190609 × 1011
Minimum2.0190608 × 1011
Maximum2.0190609 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:36.053535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190608 × 1011
5-th percentile2.0190609 × 1011
Q12.0190609 × 1011
median2.0190609 × 1011
Q32.0190609 × 1011
95-th percentile2.0190609 × 1011
Maximum2.0190609 × 1011
Range9860
Interquartile range (IQR)1415

Descriptive statistics

Standard deviation1666.7933
Coefficient of variation (CV)8.2552897 × 10-9
Kurtosis18.687775
Mean2.0190609 × 1011
Median Absolute Deviation (MAD)705
Skewness-4.0042877
Sum2.0190609 × 1013
Variance2778199.8
MonotonicityNot monotonic
2023-12-10T22:15:36.302583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201906090550 1
 
1.0%
201906092130 1
 
1.0%
201906091820 1
 
1.0%
201906091850 1
 
1.0%
201906091900 1
 
1.0%
201906091920 1
 
1.0%
201906091930 1
 
1.0%
201906092010 1
 
1.0%
201906092020 1
 
1.0%
201906092040 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201906082340 1
1.0%
201906082350 1
1.0%
201906082400 1
1.0%
201906090010 1
1.0%
201906090020 1
1.0%
201906090030 1
1.0%
201906090040 1
1.0%
201906090050 1
1.0%
201906090100 1
1.0%
201906090110 1
1.0%
ValueCountFrequency (%)
201906092200 1
1.0%
201906092150 1
1.0%
201906092140 1
1.0%
201906092130 1
1.0%
201906092120 1
1.0%
201906092110 1
1.0%
201906092100 1
1.0%
201906092050 1
1.0%
201906092040 1
1.0%
201906092030 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

2023-12-10T22:15:36.503525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:15:36.643543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.13448
Minimum1.123
Maximum1.158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:36.785996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.123
5-th percentile1.123
Q11.123
median1.13
Q31.144
95-th percentile1.158
Maximum1.158
Range0.035
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.012367667
Coefficient of variation (CV)0.010901618
Kurtosis-1.0636135
Mean1.13448
Median Absolute Deviation (MAD)0.007
Skewness0.62125246
Sum113.448
Variance0.00015295919
MonotonicityNot monotonic
2023-12-10T22:15:36.943843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1.123 42
42.0%
1.13 15
 
15.0%
1.144 14
 
14.0%
1.151 11
 
11.0%
1.137 9
 
9.0%
1.158 9
 
9.0%
ValueCountFrequency (%)
1.123 42
42.0%
1.13 15
 
15.0%
1.137 9
 
9.0%
1.144 14
 
14.0%
1.151 11
 
11.0%
1.158 9
 
9.0%
ValueCountFrequency (%)
1.158 9
 
9.0%
1.151 11
 
11.0%
1.144 14
 
14.0%
1.137 9
 
9.0%
1.13 15
 
15.0%
1.123 42
42.0%

유입량(ms)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1.6
100 

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 (%)
1.6 100
100.0%

Length

2023-12-10T22:15:37.191131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:15:37.321211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.6 100
100.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.22549
Minimum5.344
Maximum20.145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:37.513557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.344
5-th percentile6.46965
Q19
median9
Q312
95-th percentile14.5643
Maximum20.145
Range14.801
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6610935
Coefficient of variation (CV)0.26024117
Kurtosis3.1466693
Mean10.22549
Median Absolute Deviation (MAD)1
Skewness1.364296
Sum1022.549
Variance7.0814187
MonotonicityNot monotonic
2023-12-10T22:15:37.768943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
9.0 39
39.0%
10.0 9
 
9.0%
11.0 8
 
8.0%
12.0 8
 
8.0%
12.5 6
 
6.0%
10.5 3
 
3.0%
14.372 1
 
1.0%
8.378 1
 
1.0%
6.744 1
 
1.0%
6.911 1
 
1.0%
Other values (23) 23
23.0%
ValueCountFrequency (%)
5.344 1
1.0%
5.678 1
1.0%
6.011 1
1.0%
6.144 1
1.0%
6.311 1
1.0%
6.478 1
1.0%
6.744 1
1.0%
6.911 1
1.0%
7.078 1
1.0%
7.278 1
1.0%
ValueCountFrequency (%)
20.145 1
1.0%
19.644 1
1.0%
19.144 1
1.0%
14.889 1
1.0%
14.722 1
1.0%
14.556 1
1.0%
14.372 1
1.0%
14.206 1
1.0%
14.039 1
1.0%
13.889 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.22051
Minimum9
Maximum12.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:37.955004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9
Q19
median10
Q311
95-th percentile12.5
Maximum12.5
Range3.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2415887
Coefficient of variation (CV)0.12148011
Kurtosis-1.1050444
Mean10.22051
Median Absolute Deviation (MAD)1
Skewness0.53038641
Sum1022.051
Variance1.5415424
MonotonicityNot monotonic
2023-12-10T22:15:38.144869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
9.0 41
41.0%
10.0 14
 
14.0%
11.0 12
 
12.0%
12.0 10
 
10.0%
10.5 7
 
7.0%
12.5 7
 
7.0%
10.667 1
 
1.0%
10.1 1
 
1.0%
11.5 1
 
1.0%
9.9 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
9.0 41
41.0%
9.9 1
 
1.0%
10.0 14
 
14.0%
10.1 1
 
1.0%
10.15 1
 
1.0%
10.5 7
 
7.0%
10.667 1
 
1.0%
10.967 1
 
1.0%
11.0 12
 
12.0%
11.5 1
 
1.0%
ValueCountFrequency (%)
12.5 7
7.0%
12.367 1
 
1.0%
12.3 1
 
1.0%
12.1 1
 
1.0%
12.0 10
10.0%
11.5 1
 
1.0%
11.0 12
12.0%
10.967 1
 
1.0%
10.667 1
 
1.0%
10.5 7
7.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2264
Minimum23.21
Maximum23.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:38.364175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.21
5-th percentile23.21
Q123.21
median23.22
Q323.24
95-th percentile23.26
Maximum23.26
Range0.05
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.017668096
Coefficient of variation (CV)0.00076069025
Kurtosis-1.0636135
Mean23.2264
Median Absolute Deviation (MAD)0.01
Skewness0.62125246
Sum2322.64
Variance0.00031216162
MonotonicityNot monotonic
2023-12-10T22:15:38.583126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
23.21 42
42.0%
23.22 15
 
15.0%
23.24 14
 
14.0%
23.25 11
 
11.0%
23.23 9
 
9.0%
23.26 9
 
9.0%
ValueCountFrequency (%)
23.21 42
42.0%
23.22 15
 
15.0%
23.23 9
 
9.0%
23.24 14
 
14.0%
23.25 11
 
11.0%
23.26 9
 
9.0%
ValueCountFrequency (%)
23.26 9
 
9.0%
23.25 11
 
11.0%
23.24 14
 
14.0%
23.23 9
 
9.0%
23.22 15
 
15.0%
23.21 42
42.0%

Interactions

2023-12-10T22:15:34.373815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:30.817617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:31.577789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:32.423158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:33.610585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:34.510583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:30.951119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:31.765259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:32.565553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:33.753191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:34.650382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:31.156240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:31.950799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:32.705799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:33.899964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:34.805548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:31.297841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:32.131095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:32.890316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:34.060049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:34.992658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:31.437046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:32.294983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:33.475257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:34.212574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:15:38.730888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.4630.7990.5560.463
강우량(mm)0.4631.0000.8150.9621.000
방류량(ms)0.7990.8151.0000.9030.815
저수량(백만m3)0.5560.9620.9031.0000.962
저수율0.4631.0000.8150.9621.000
2023-12-10T22:15:38.922338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.297-0.0190.3150.297
강우량(mm)0.2971.0000.6020.9951.000
방류량(ms)-0.0190.6021.0000.5690.602
저수량(백만m3)0.3150.9950.5691.0000.995
저수율0.2971.0000.6020.9951.000

Missing values

2023-12-10T22:15:35.224926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:15:35.479033image/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
0군남20190609055001.1371.614.37210.523.23
1군남20190608240001.1231.69.09.023.21
2군남20190609170001.1441.611.011.023.24
3군남20190609060001.1441.614.55610.66723.24
4군남20190609024001.1231.69.09.023.21
5군남20190609001001.1231.69.09.023.21
6군남20190609194001.131.610.010.023.22
7군남20190609171001.1441.611.011.023.24
8군남20190609143001.1511.612.012.023.25
9군남20190609061001.1441.614.72211.023.24
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군남20190609072001.1581.612.512.523.26
91군남20190609071001.1581.612.512.523.26
92군남20190609065001.1581.619.64412.523.26
93군남20190609064001.1581.619.14412.123.26
94군남20190609054001.1371.614.20610.523.23
95군남20190609053001.1371.614.03910.1523.23
96군남20190609051001.131.613.55610.023.22
97군남20190609050001.131.613.22210.023.22
98군남20190608234001.1231.69.09.023.21
99군남20190608235001.1231.69.09.023.21