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
저수율 has constant value ""Constant
일자/시간(t) is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
저수위(m) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
강우량(mm) is highly imbalanced (80.6%)Imbalance
일자/시간(t) has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:15:21.201375
Analysis finished2023-12-10 13:15:26.187611
Duration4.99 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

2023-12-10T22:15:26.281726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:15:26.405425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.019071 × 1011
Minimum2.019071 × 1011
Maximum2.0190711 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:26.569312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.019071 × 1011
5-th percentile2.019071 × 1011
Q12.019071 × 1011
median2.019071 × 1011
Q32.019071 × 1011
95-th percentile2.0190711 × 1011
Maximum2.0190711 × 1011
Range10130
Interquartile range (IQR)1715

Descriptive statistics

Standard deviation2843.1276
Coefficient of variation (CV)1.4081365 × 10-8
Kurtosis4.3877435
Mean2.019071 × 1011
Median Absolute Deviation (MAD)520
Skewness2.393191
Sum2.019071 × 1013
Variance8083374.7
MonotonicityNot monotonic
2023-12-10T22:15:26.869769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201907100100 1
 
1.0%
201907101050 1
 
1.0%
201907100740 1
 
1.0%
201907100810 1
 
1.0%
201907100820 1
 
1.0%
201907100840 1
 
1.0%
201907100850 1
 
1.0%
201907100930 1
 
1.0%
201907100940 1
 
1.0%
201907101000 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201907100010 1
1.0%
201907100020 1
1.0%
201907100030 1
1.0%
201907100040 1
1.0%
201907100050 1
1.0%
201907100100 1
1.0%
201907100110 1
1.0%
201907100120 1
1.0%
201907100130 1
1.0%
201907100140 1
1.0%
ValueCountFrequency (%)
201907110140 1
1.0%
201907110130 1
1.0%
201907110120 1
1.0%
201907110110 1
1.0%
201907110100 1
1.0%
201907110050 1
1.0%
201907110040 1
1.0%
201907110030 1
1.0%
201907110020 1
1.0%
201907110010 1
1.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2133
Minimum23.19
Maximum23.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:27.042704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.19
5-th percentile23.19
Q123.19
median23.21
Q323.24
95-th percentile23.25
Maximum23.25
Range0.06
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.022475801
Coefficient of variation (CV)0.00096822945
Kurtosis-1.085713
Mean23.2133
Median Absolute Deviation (MAD)0.02
Skewness0.62302513
Sum2321.33
Variance0.00050516162
MonotonicityNot monotonic
2023-12-10T22:15:27.208748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
23.19 30
30.0%
23.21 28
28.0%
23.25 19
19.0%
23.2 11
 
11.0%
23.24 8
 
8.0%
23.22 4
 
4.0%
ValueCountFrequency (%)
23.19 30
30.0%
23.2 11
 
11.0%
23.21 28
28.0%
23.22 4
 
4.0%
23.24 8
 
8.0%
23.25 19
19.0%
ValueCountFrequency (%)
23.25 19
19.0%
23.24 8
 
8.0%
23.22 4
 
4.0%
23.21 28
28.0%
23.2 11
 
11.0%
23.19 30
30.0%

강우량(mm)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
97 
1
 
3

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 97
97.0%
1 3
 
3.0%

Length

2023-12-10T22:15:27.408699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:15:27.564925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 97
97.0%
1 3
 
3.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.69014
Minimum8.183
Maximum23.108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:27.732441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.183
5-th percentile11.2
Q111.2
median12.6
Q316.41825
95-th percentile19.45235
Maximum23.108
Range14.925
Interquartile range (IQR)5.21825

Descriptive statistics

Standard deviation3.0222809
Coefficient of variation (CV)0.22076333
Kurtosis0.73264132
Mean13.69014
Median Absolute Deviation (MAD)1.4
Skewness0.9772777
Sum1369.014
Variance9.1341818
MonotonicityNot monotonic
2023-12-10T22:15:27.978692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11.2 30
30.0%
12.6 25
25.0%
16.8 16
16.0%
15.4 5
 
5.0%
11.9 5
 
5.0%
15.346 1
 
1.0%
15.579 1
 
1.0%
16.083 1
 
1.0%
16.317 1
 
1.0%
22.174 1
 
1.0%
Other values (14) 14
14.0%
ValueCountFrequency (%)
8.183 1
 
1.0%
8.417 1
 
1.0%
8.65 1
 
1.0%
11.2 30
30.0%
11.9 5
 
5.0%
12.6 25
25.0%
14.0 1
 
1.0%
15.112 1
 
1.0%
15.346 1
 
1.0%
15.4 5
 
5.0%
ValueCountFrequency (%)
23.108 1
 
1.0%
22.641 1
 
1.0%
22.174 1
 
1.0%
20.362 1
 
1.0%
19.896 1
 
1.0%
19.429 1
 
1.0%
17.656 1
 
1.0%
17.189 1
 
1.0%
16.8 16
16.0%
16.722 1
 
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.1467
Minimum11.2
Maximum16.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:28.158968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.2
5-th percentile11.2
Q111.2
median12.6
Q315.4
95-th percentile16.8
Maximum16.8
Range5.6
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation2.0842129
Coefficient of variation (CV)0.15853506
Kurtosis-0.85952614
Mean13.1467
Median Absolute Deviation (MAD)1.4
Skewness0.83591631
Sum1314.67
Variance4.3439435
MonotonicityNot monotonic
2023-12-10T22:15:28.424598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11.2 30
30.0%
12.6 27
27.0%
16.8 18
18.0%
11.9 8
 
8.0%
15.4 7
 
7.0%
13.3 2
 
2.0%
14.0 2
 
2.0%
12.25 2
 
2.0%
11.27 1
 
1.0%
15.82 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
11.2 30
30.0%
11.27 1
 
1.0%
11.69 1
 
1.0%
11.9 8
 
8.0%
12.25 2
 
2.0%
12.6 27
27.0%
13.3 2
 
2.0%
14.0 2
 
2.0%
15.19 1
 
1.0%
15.4 7
 
7.0%
ValueCountFrequency (%)
16.8 18
18.0%
15.82 1
 
1.0%
15.4 7
 
7.0%
15.19 1
 
1.0%
14.0 2
 
2.0%
13.3 2
 
2.0%
12.6 27
27.0%
12.25 2
 
2.0%
11.9 8
 
8.0%
11.69 1
 
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.12531
Minimum1.109
Maximum1.151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:15:28.655345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.109
5-th percentile1.109
Q11.109
median1.123
Q31.144
95-th percentile1.151
Maximum1.151
Range0.042
Interquartile range (IQR)0.035

Descriptive statistics

Standard deviation0.01573306
Coefficient of variation (CV)0.01398109
Kurtosis-1.085713
Mean1.12531
Median Absolute Deviation (MAD)0.014
Skewness0.62302513
Sum112.531
Variance0.00024752919
MonotonicityNot monotonic
2023-12-10T22:15:28.891065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1.109 30
30.0%
1.123 28
28.0%
1.151 19
19.0%
1.116 11
 
11.0%
1.144 8
 
8.0%
1.13 4
 
4.0%
ValueCountFrequency (%)
1.109 30
30.0%
1.116 11
 
11.0%
1.123 28
28.0%
1.13 4
 
4.0%
1.144 8
 
8.0%
1.151 19
19.0%
ValueCountFrequency (%)
1.151 19
19.0%
1.144 8
 
8.0%
1.13 4
 
4.0%
1.123 28
28.0%
1.116 11
 
11.0%
1.109 30
30.0%

저수율
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

2023-12-10T22:15:29.107609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:15:29.279005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.6 100
100.0%

Interactions

2023-12-10T22:15:25.076023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:21.544784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:22.301210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:23.397266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:24.283063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:25.275573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:21.691063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:22.499694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:23.588518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:24.434145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:25.426940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:21.832145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:22.661746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:23.742315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:24.574968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:25.588057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:21.999992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:22.795336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:23.896046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:24.767032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:25.728580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:22.164651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:23.275522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:24.126004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:15:24.933773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:15:29.375904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)
일자/시간(t)1.0000.8000.4420.5690.9350.800
저수위(m)0.8001.0000.2450.8960.9911.000
강우량(mm)0.4420.2451.0000.0000.1610.245
유입량(ms)0.5690.8960.0001.0000.8620.896
방류량(ms)0.9350.9910.1610.8621.0000.991
저수량(백만m3)0.8001.0000.2450.8960.9911.000
2023-12-10T22:15:29.551379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)강우량(mm)
일자/시간(t)1.0000.5090.3160.5230.5090.295
저수위(m)0.5091.0000.9250.9981.0000.171
유입량(ms)0.3160.9251.0000.9110.9250.000
방류량(ms)0.5230.9980.9111.0000.9980.114
저수량(백만m3)0.5091.0000.9250.9981.0000.171
강우량(mm)0.2950.1710.0000.1140.1711.000

Missing values

2023-12-10T22:15:25.910004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:15:26.115482image/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
0군남20190710010023.19011.211.21.1091.6
1군남20190710205023.21012.612.61.1231.6
2군남20190710062023.22016.72213.31.131.6
3군남20190710011023.19011.211.21.1091.6
4군남20190710233023.21012.612.61.1231.6
5군남20190710210023.21012.612.61.1231.6
6군남20190710090023.25016.816.81.1511.6
7군남20190710063023.22017.18913.31.131.6
8군남20190710035023.19011.211.21.1091.6
9군남20190710012023.19011.211.21.1091.6
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군남20190710023023.19011.211.21.1091.6
91군남20190710022023.19011.211.21.1091.6
92군남20190710020023.19011.211.21.1091.6
93군남20190710015023.19011.211.21.1091.6
94군남20190710005023.19011.211.21.1091.6
95군남20190710004023.19011.211.21.1091.6
96군남20190710002023.19011.211.21.1091.6
97군남20190710001023.19011.211.21.1091.6
98군남20190710203023.21012.612.61.1231.6
99군남20190710204023.21012.612.61.1231.6