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
강우량(mm) has constant value ""Constant
저수위(m) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
유입량(ms) has 7 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:17:49.500568
Analysis finished2023-12-10 13:17:55.584490
Duration6.08 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
강천보
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강천보 100
100.0%

Length

2023-12-10T22:17:55.720211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:17:55.865870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 100
100.0%

일자/시간(t)
Real number (ℝ)

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0191203 × 1011
Minimum2.0191202 × 1011
Maximum2.0191206 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:17:56.042210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0191202 × 1011
5-th percentile2.0191202 × 1011
Q12.0191202 × 1011
median2.0191202 × 1011
Q32.0191205 × 1011
95-th percentile2.0191206 × 1011
Maximum2.0191206 × 1011
Range39690
Interquartile range (IQR)31482.5

Descriptive statistics

Standard deviation16139.731
Coefficient of variation (CV)7.9934469 × 10-8
Kurtosis-1.1396266
Mean2.0191203 × 1011
Median Absolute Deviation (MAD)375
Skewness0.89203228
Sum2.0191203 × 1013
Variance2.6049092 × 108
MonotonicityNot monotonic
2023-12-10T22:17:57.508559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201912052200 2
 
2.0%
201912060100 2
 
2.0%
201912021230 2
 
2.0%
201912021100 2
 
2.0%
201912021030 2
 
2.0%
201912020900 2
 
2.0%
201912020830 2
 
2.0%
201912020500 2
 
2.0%
201912060130 2
 
2.0%
201912060030 2
 
2.0%
Other values (65) 80
80.0%
ValueCountFrequency (%)
201912020440 1
1.0%
201912020450 1
1.0%
201912020500 2
2.0%
201912020510 1
1.0%
201912020520 1
1.0%
201912020530 2
2.0%
201912020540 1
1.0%
201912020550 1
1.0%
201912020600 2
2.0%
201912020610 1
1.0%
ValueCountFrequency (%)
201912060130 2
2.0%
201912060120 1
1.0%
201912060110 1
1.0%
201912060100 2
2.0%
201912060050 1
1.0%
201912060040 1
1.0%
201912060030 2
2.0%
201912060020 1
1.0%
201912060010 1
1.0%
201912052400 2
2.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.1285
Minimum38.1
Maximum38.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:17:58.319558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.1
5-th percentile38.1
Q138.11
median38.13
Q338.15
95-th percentile38.16
Maximum38.16
Range0.06
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.02275806
Coefficient of variation (CV)0.00059687792
Kurtosis-1.5680829
Mean38.1285
Median Absolute Deviation (MAD)0.02
Skewness0.23616602
Sum3812.85
Variance0.00051792929
MonotonicityNot monotonic
2023-12-10T22:17:58.661587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
38.11 35
35.0%
38.16 24
24.0%
38.14 17
17.0%
38.1 14
 
14.0%
38.13 6
 
6.0%
38.15 4
 
4.0%
ValueCountFrequency (%)
38.1 14
 
14.0%
38.11 35
35.0%
38.13 6
 
6.0%
38.14 17
17.0%
38.15 4
 
4.0%
38.16 24
24.0%
ValueCountFrequency (%)
38.16 24
24.0%
38.15 4
 
4.0%
38.14 17
17.0%
38.13 6
 
6.0%
38.11 35
35.0%
38.1 14
 
14.0%

강우량(mm)
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

2023-12-10T22:17:58.981572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:17:59.115312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.86564
Minimum0
Maximum246.754
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:17:59.281166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q188.617
median90.7165
Q398.873
95-th percentile221.12805
Maximum246.754
Range246.754
Interquartile range (IQR)10.256

Descriptive statistics

Standard deviation47.596463
Coefficient of variation (CV)0.48142573
Kurtosis3.5522428
Mean98.86564
Median Absolute Deviation (MAD)4.1765
Skewness1.0123533
Sum9886.564
Variance2265.4233
MonotonicityNot monotonic
2023-12-10T22:17:59.589141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
86.54 3
 
3.0%
94.693 3
 
3.0%
94.687 3
 
3.0%
88.687 3
 
3.0%
88.713 2
 
2.0%
86.627 2
 
2.0%
98.74 2
 
2.0%
98.873 2
 
2.0%
88.66 2
 
2.0%
Other values (61) 71
71.0%
ValueCountFrequency (%)
0.0 7
7.0%
12.372 1
 
1.0%
71.974 1
 
1.0%
71.975 1
 
1.0%
72.778 1
 
1.0%
73.505 1
 
1.0%
86.54 3
3.0%
86.56 1
 
1.0%
86.61 1
 
1.0%
86.627 2
 
2.0%
ValueCountFrequency (%)
246.754 2
2.0%
242.698 1
1.0%
238.718 1
1.0%
221.129 1
1.0%
221.128 1
1.0%
217.712 1
1.0%
174.022 1
1.0%
139.103 1
1.0%
123.62 1
1.0%
123.619 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.27095
Minimum86.51
Maximum98.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:17:59.802461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86.51
5-th percentile86.56
Q188.64
median90.969
Q396.03375
95-th percentile98.84165
Maximum98.95
Range12.44
Interquartile range (IQR)7.39375

Descriptive statistics

Standard deviation4.4141533
Coefficient of variation (CV)0.047839036
Kurtosis-1.469661
Mean92.27095
Median Absolute Deviation (MAD)3.696
Skewness0.30785265
Sum9227.095
Variance19.484749
MonotonicityNot monotonic
2023-12-10T22:18:00.061962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.64 4
 
4.0%
88.65 3
 
3.0%
88.66 3
 
3.0%
88.6 2
 
2.0%
94.69 2
 
2.0%
88.74 2
 
2.0%
88.68 2
 
2.0%
86.56 2
 
2.0%
88.63 2
 
2.0%
98.8 2
 
2.0%
Other values (71) 76
76.0%
ValueCountFrequency (%)
86.51 1
1.0%
86.54 1
1.0%
86.55 2
2.0%
86.56 2
2.0%
86.57 1
1.0%
86.627 1
1.0%
86.69 1
1.0%
86.762 1
1.0%
86.77 1
1.0%
87.398 1
1.0%
ValueCountFrequency (%)
98.95 1
1.0%
98.9 2
2.0%
98.88 1
1.0%
98.873 1
1.0%
98.84 1
1.0%
98.83 1
1.0%
98.81 1
1.0%
98.8 2
2.0%
98.773 1
1.0%
98.77 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3097
Minimum9.181
Maximum9.452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:18:00.264962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.181
5-th percentile9.181
Q19.226
median9.316
Q39.407
95-th percentile9.452
Maximum9.452
Range0.271
Interquartile range (IQR)0.181

Descriptive statistics

Standard deviation0.10287194
Coefficient of variation (CV)0.011049974
Kurtosis-1.5708139
Mean9.3097
Median Absolute Deviation (MAD)0.09
Skewness0.23580313
Sum930.97
Variance0.010582636
MonotonicityNot monotonic
2023-12-10T22:18:00.455062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9.226 35
35.0%
9.452 24
24.0%
9.362 17
17.0%
9.181 14
 
14.0%
9.316 6
 
6.0%
9.407 4
 
4.0%
ValueCountFrequency (%)
9.181 14
 
14.0%
9.226 35
35.0%
9.316 6
 
6.0%
9.362 17
17.0%
9.407 4
 
4.0%
9.452 24
24.0%
ValueCountFrequency (%)
9.452 24
24.0%
9.407 4
 
4.0%
9.362 17
17.0%
9.316 6
 
6.0%
9.226 35
35.0%
9.181 14
 
14.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.67
Minimum105.2
Maximum108.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:18:00.630744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105.2
5-th percentile105.2
Q1105.7
median106.7
Q3107.8
95-th percentile108.3
Maximum108.3
Range3.1
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.184112
Coefficient of variation (CV)0.011100703
Kurtosis-1.5919863
Mean106.67
Median Absolute Deviation (MAD)1
Skewness0.23308774
Sum10667
Variance1.4021212
MonotonicityNot monotonic
2023-12-10T22:18:00.777316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
105.7 35
35.0%
108.3 24
24.0%
107.3 17
17.0%
105.2 14
 
14.0%
106.7 6
 
6.0%
107.8 4
 
4.0%
ValueCountFrequency (%)
105.2 14
 
14.0%
105.7 35
35.0%
106.7 6
 
6.0%
107.3 17
17.0%
107.8 4
 
4.0%
108.3 24
24.0%
ValueCountFrequency (%)
108.3 24
24.0%
107.8 4
 
4.0%
107.3 17
17.0%
106.7 6
 
6.0%
105.7 35
35.0%
105.2 14
 
14.0%

Interactions

2023-12-10T22:17:54.315662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:49.730039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:50.660974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:51.591058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:52.479281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:53.434207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:54.488926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:49.925777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:50.816563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:51.764777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:52.656745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:53.614708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:54.632049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:50.085195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:50.953031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:51.930870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:52.821689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:53.768648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:54.761578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:50.232615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:51.088160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:52.052822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:52.970232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:53.900580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:54.902816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:50.377497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:51.236911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:52.190991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:53.121918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:54.039908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:55.109184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:50.513429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:51.389676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:52.320463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:53.275253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:54.176384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:18:00.916900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.6530.6090.6720.6530.653
저수위(m)0.6531.0000.8090.9651.0001.000
유입량(ms)0.6090.8091.0000.7230.8090.809
방류량(ms)0.6720.9650.7231.0000.9650.965
저수량(백만m3)0.6531.0000.8090.9651.0001.000
저수율0.6531.0000.8090.9651.0001.000
2023-12-10T22:18:01.121220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.2140.1900.1820.2140.214
저수위(m)0.2141.0000.7600.9181.0001.000
유입량(ms)0.1900.7601.0000.6750.7600.760
방류량(ms)0.1820.9180.6751.0000.9180.918
저수량(백만m3)0.2141.0000.7600.9181.0001.000
저수율0.2141.0000.7600.9181.0001.000

Missing values

2023-12-10T22:17:55.315638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:17:55.511759image/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
0강천보20191205220038.130116.94992.589.316106.7
1강천보20191205221038.130117.67592.759.316106.7
2강천보20191205223038.140117.97392.8629.362107.3
3강천보20191205223038.140117.97293.0949.362107.3
4강천보20191205222038.13092.6992.749.316106.7
5강천보20191205230038.14094.74794.619.362107.3
6강천보20191202060038.100.093.0819.181105.2
7강천보20191202094038.11088.73388.799.226105.7
8강천보20191202060038.100.089.8329.181105.2
9강천보20191205225038.140119.35294.89.362107.3
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90강천보20191202070038.11088.6488.649.226105.7
91강천보20191202070038.11088.6488.669.226105.7
92강천보20191202064038.110113.20488.619.226105.7
93강천보20191202063038.110112.56587.3989.226105.7
94강천보20191202054038.14094.69394.699.362107.3
95강천보20191202055038.14094.68794.729.362107.3
96강천보20191202053038.14094.69394.6939.362107.3
97강천보20191202053038.14094.69394.659.362107.3
98강천보20191205215038.130116.30592.1969.316106.7
99강천보20191205220038.130116.94991.7829.316106.7