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 방류량(ms)High correlation
강우량(mm) is highly overall correlated with 저수율(ms) and 1 other fieldsHigh correlation
저수량 is highly overall correlated with 방류량(ms)High correlation
저수율(ms) is highly overall correlated with 강우량(mm) and 1 other fieldsHigh correlation
유입량(백만m3) is highly overall correlated with 강우량(mm) and 1 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 일자/시간(t) and 1 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
저수량 has 4 (4.0%) zerosZeros
방류량(ms) has 6 (6.0%) zerosZeros

Reproduction

Analysis started2024-04-17 06:02:38.126713
Analysis finished2024-04-17 06:02:41.391862
Duration3.27 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

2024-04-17T15:02:41.446934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:02:41.519058image/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%
Mean2.0190512 × 109
Minimum2.0190501 × 109
Maximum2.0190518 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:41.610239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190501 × 109
5-th percentile2.0190501 × 109
Q12.0190511 × 109
median2.0190513 × 109
Q32.0190515 × 109
95-th percentile2.0190517 × 109
Maximum2.0190518 × 109
Range1700
Interquartile range (IQR)422

Descriptive statistics

Standard deviation537.07808
Coefficient of variation (CV)2.6600518 × 10-7
Kurtosis0.023714551
Mean2.0190512 × 109
Median Absolute Deviation (MAD)202
Skewness-1.2049146
Sum2.0190512 × 1011
Variance288452.87
MonotonicityNot monotonic
2024-04-17T15:02:41.748421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019051412 1
 
1.0%
2019050208 1
 
1.0%
2019051720 1
 
1.0%
2019051802 1
 
1.0%
2019050102 1
 
1.0%
2019050106 1
 
1.0%
2019050108 1
 
1.0%
2019050116 1
 
1.0%
2019050118 1
 
1.0%
2019050122 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019050102 1
1.0%
2019050104 1
1.0%
2019050106 1
1.0%
2019050108 1
1.0%
2019050110 1
1.0%
2019050112 1
1.0%
2019050114 1
1.0%
2019050116 1
1.0%
2019050118 1
1.0%
2019050120 1
1.0%
ValueCountFrequency (%)
2019051802 1
1.0%
2019051724 1
1.0%
2019051722 1
1.0%
2019051720 1
1.0%
2019051718 1
1.0%
2019051716 1
1.0%
2019051714 1
1.0%
2019051712 1
1.0%
2019051710 1
1.0%
2019051708 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

2024-04-17T15:02:42.143800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:02:42.213853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.01442
Minimum2.006
Maximum2.018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:42.282219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.006
5-th percentile2.009
Q12.013
median2.015
Q32.017
95-th percentile2.017
Maximum2.018
Range0.012
Interquartile range (IQR)0.004

Descriptive statistics

Standard deviation0.0025193393
Coefficient of variation (CV)0.0012506525
Kurtosis1.6801655
Mean2.01442
Median Absolute Deviation (MAD)0.002
Skewness-1.2383354
Sum201.442
Variance6.3470707 × 10-6
MonotonicityNot monotonic
2024-04-17T15:02:42.376287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.015 41
41.0%
2.017 25
25.0%
2.013 17
17.0%
2.011 9
 
9.0%
2.009 2
 
2.0%
2.006 2
 
2.0%
2.008 2
 
2.0%
2.018 2
 
2.0%
ValueCountFrequency (%)
2.006 2
 
2.0%
2.008 2
 
2.0%
2.009 2
 
2.0%
2.011 9
 
9.0%
2.013 17
17.0%
2.015 41
41.0%
2.017 25
25.0%
2.018 2
 
2.0%
ValueCountFrequency (%)
2.018 2
 
2.0%
2.017 25
25.0%
2.015 41
41.0%
2.013 17
17.0%
2.011 9
 
9.0%
2.009 2
 
2.0%
2.008 2
 
2.0%
2.006 2
 
2.0%

저수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01877
Minimum0
Maximum0.067
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:42.474179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00985
Q10.01
median0.0125
Q30.02475
95-th percentile0.05
Maximum0.067
Range0.067
Interquartile range (IQR)0.01475

Descriptive statistics

Standard deviation0.013065379
Coefficient of variation (CV)0.69607776
Kurtosis2.8419692
Mean0.01877
Median Absolute Deviation (MAD)0.0075
Skewness1.5686986
Sum1.877
Variance0.00017070414
MonotonicityNot monotonic
2024-04-17T15:02:42.571045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.01 45
45.0%
0.02 20
20.0%
0.03 14
 
14.0%
0.0 4
 
4.0%
0.05 2
 
2.0%
0.017 2
 
2.0%
0.06 2
 
2.0%
0.055 1
 
1.0%
0.035 1
 
1.0%
0.027 1
 
1.0%
Other values (8) 8
 
8.0%
ValueCountFrequency (%)
0.0 4
 
4.0%
0.007 1
 
1.0%
0.01 45
45.0%
0.015 1
 
1.0%
0.017 2
 
2.0%
0.02 20
20.0%
0.022 1
 
1.0%
0.024 1
 
1.0%
0.027 1
 
1.0%
0.029 1
 
1.0%
ValueCountFrequency (%)
0.067 1
 
1.0%
0.06 2
 
2.0%
0.055 1
 
1.0%
0.05 2
 
2.0%
0.039 1
 
1.0%
0.035 1
 
1.0%
0.033 1
 
1.0%
0.03 14
14.0%
0.029 1
 
1.0%
0.027 1
 
1.0%

저수율(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.027
Minimum37.98
Maximum38.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:42.668300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.98
5-th percentile38
Q138.02
median38.03
Q338.04
95-th percentile38.04
Maximum38.05
Range0.07
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.013371158
Coefficient of variation (CV)0.00035162275
Kurtosis2.4937657
Mean38.027
Median Absolute Deviation (MAD)0.01
Skewness-1.3710672
Sum3802.7
Variance0.00017878788
MonotonicityNot monotonic
2024-04-17T15:02:42.759036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
38.03 41
41.0%
38.04 25
25.0%
38.02 17
17.0%
38.01 9
 
9.0%
38.0 2
 
2.0%
37.98 2
 
2.0%
37.99 2
 
2.0%
38.05 2
 
2.0%
ValueCountFrequency (%)
37.98 2
 
2.0%
37.99 2
 
2.0%
38.0 2
 
2.0%
38.01 9
 
9.0%
38.02 17
17.0%
38.03 41
41.0%
38.04 25
25.0%
38.05 2
 
2.0%
ValueCountFrequency (%)
38.05 2
 
2.0%
38.04 25
25.0%
38.03 41
41.0%
38.02 17
17.0%
38.01 9
 
9.0%
38.0 2
 
2.0%
37.99 2
 
2.0%
37.98 2
 
2.0%

유입량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.406
Minimum76.1
Maximum76.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:42.857135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76.1
5-th percentile76.2
Q176.4
median76.4
Q376.5
95-th percentile76.5
Maximum76.6
Range0.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.087409405
Coefficient of variation (CV)0.0011440123
Kurtosis2.5435971
Mean76.406
Median Absolute Deviation (MAD)0
Skewness-1.0438721
Sum7640.6
Variance0.007640404
MonotonicityNot monotonic
2024-04-17T15:02:42.957883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
76.4 58
58.0%
76.5 25
25.0%
76.3 9
 
9.0%
76.2 4
 
4.0%
76.1 2
 
2.0%
76.6 2
 
2.0%
ValueCountFrequency (%)
76.1 2
 
2.0%
76.2 4
 
4.0%
76.3 9
 
9.0%
76.4 58
58.0%
76.5 25
25.0%
76.6 2
 
2.0%
ValueCountFrequency (%)
76.6 2
 
2.0%
76.5 25
25.0%
76.4 58
58.0%
76.3 9
 
9.0%
76.2 4
 
4.0%
76.1 2
 
2.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04289
Minimum0
Maximum0.53
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:43.052501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.01
Q30.02325
95-th percentile0.08845
Maximum0.53
Range0.53
Interquartile range (IQR)0.01325

Descriptive statistics

Standard deviation0.1115101
Coefficient of variation (CV)2.5999091
Kurtosis15.469837
Mean0.04289
Median Absolute Deviation (MAD)0.01
Skewness4.1103934
Sum4.289
Variance0.012434503
MonotonicityNot monotonic
2024-04-17T15:02:43.158842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.01 44
44.0%
0.02 20
20.0%
0.03 10
 
10.0%
0.0 6
 
6.0%
0.53 4
 
4.0%
0.017 2
 
2.0%
0.06 2
 
2.0%
0.055 1
 
1.0%
0.035 1
 
1.0%
0.027 1
 
1.0%
Other values (9) 9
 
9.0%
ValueCountFrequency (%)
0.0 6
 
6.0%
0.007 1
 
1.0%
0.01 44
44.0%
0.015 1
 
1.0%
0.017 2
 
2.0%
0.02 20
20.0%
0.022 1
 
1.0%
0.027 1
 
1.0%
0.029 1
 
1.0%
0.03 10
 
10.0%
ValueCountFrequency (%)
0.53 4
 
4.0%
0.496 1
 
1.0%
0.067 1
 
1.0%
0.06 2
 
2.0%
0.055 1
 
1.0%
0.05 1
 
1.0%
0.039 1
 
1.0%
0.035 1
 
1.0%
0.033 1
 
1.0%
0.03 10
10.0%

Interactions

2024-04-17T15:02:40.730548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.316453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.764898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.257018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.736908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.230736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.807988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.387683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.841760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.342887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.819504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.314880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.887664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.461199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.917819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.418013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.905574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.396373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.971222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.537166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.995915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.499645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.995803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.497122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:41.059159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.616178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.086911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.580307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.077132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.591563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:41.128889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:38.689162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.166980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:39.658550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.156623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:40.658187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T15:02:43.236076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
일자/시간(t)1.0000.6950.7840.7490.8630.601
강우량(mm)0.6951.0000.5441.0000.9440.000
저수량0.7840.5441.0000.5180.5770.963
저수율(ms)0.7491.0000.5181.0001.0000.000
유입량(백만m3)0.8630.9440.5771.0001.0000.000
방류량(ms)0.6010.0000.9630.0000.0001.000
2024-04-17T15:02:43.328356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
일자/시간(t)1.000-0.206-0.433-0.206-0.166-0.524
강우량(mm)-0.2061.000-0.1841.0000.931-0.123
저수량-0.433-0.1841.000-0.184-0.0960.929
저수율(ms)-0.2061.000-0.1841.0000.931-0.123
유입량(백만m3)-0.1660.931-0.0960.9311.000-0.040
방류량(ms)-0.524-0.1230.929-0.123-0.0401.000

Missing values

2024-04-17T15:02:41.241746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T15:02:41.348740image/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)
0감포201905141202.0150.0138.0376.40.01
1감포201905111402.0170.0238.0476.50.02
2감포201905170402.0130.0138.0276.40.01
3감포201905141402.0150.0138.0376.40.01
4감포201905122202.0170.0238.0476.50.02
5감포201905111602.0170.0238.0476.50.02
6감포201905011002.0090.0338.076.20.53
7감포201905170602.0130.0138.0276.40.01
8감포201905152202.0150.0138.0376.40.01
9감포201905141602.0150.0138.0376.40.01
댐이름일자/시간(t)저수위(m)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
90감포201905150602.0150.0138.0376.40.01
91감포201905150402.0150.0138.0376.40.01
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