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
강우량(mm) has constant value ""Constant
일자/시간(t) is highly overall correlated with 저수위(m) and 3 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 방류량(ms)High 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
저수율 is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
유입량(ms) has 3 (3.0%) zerosZeros
방류량(ms) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2024-04-16 18:11:10.028239
Analysis finished2024-04-16 18:11:12.566120
Duration2.54 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

2024-04-17T03:11:12.618973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:12.693269image/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%
Mean2.0190502 × 1011
Minimum2.0190501 × 1011
Maximum2.0190503 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T03:11:12.790680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190501 × 1011
5-th percentile2.0190501 × 1011
Q12.0190501 × 1011
median2.0190501 × 1011
Q32.0190502 × 1011
95-th percentile2.0190503 × 1011
Maximum2.0190503 × 1011
Range20980
Interquartile range (IQR)9270

Descriptive statistics

Standard deviation7744.2767
Coefficient of variation (CV)3.8356039 × 10-8
Kurtosis-0.1266034
Mean2.0190502 × 1011
Median Absolute Deviation (MAD)940
Skewness1.2778607
Sum2.0190502 × 1013
Variance59973822
MonotonicityNot monotonic
2024-04-17T03:11:12.909570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201905010620 1
 
1.0%
201905020200 1
 
1.0%
201905011940 1
 
1.0%
201905012040 1
 
1.0%
201905012100 1
 
1.0%
201905012140 1
 
1.0%
201905012200 1
 
1.0%
201905012320 1
 
1.0%
201905012340 1
 
1.0%
201905020020 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201905010020 1
1.0%
201905010040 1
1.0%
201905010100 1
1.0%
201905010120 1
1.0%
201905010140 1
1.0%
201905010200 1
1.0%
201905010220 1
1.0%
201905010240 1
1.0%
201905010300 1
1.0%
201905010320 1
1.0%
ValueCountFrequency (%)
201905031000 1
1.0%
201905030940 1
1.0%
201905030920 1
1.0%
201905030900 1
1.0%
201905030840 1
1.0%
201905030820 1
1.0%
201905030800 1
1.0%
201905030740 1
1.0%
201905030720 1
1.0%
201905030700 1
1.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.0112
Minimum37.98
Maximum38.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T03:11:13.020659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.98
5-th percentile37.98
Q137.99
median38.01
Q338.02
95-th percentile38.05
Maximum38.05
Range0.07
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.02248591
Coefficient of variation (CV)0.00059156013
Kurtosis-0.66756004
Mean38.0112
Median Absolute Deviation (MAD)0.01
Skewness0.53817055
Sum3801.12
Variance0.00050561616
MonotonicityNot monotonic
2024-04-17T03:11:13.114112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
38.01 24
24.0%
38.05 19
19.0%
38.02 16
16.0%
37.99 15
15.0%
38.0 14
14.0%
37.98 12
12.0%
ValueCountFrequency (%)
37.98 12
12.0%
37.99 15
15.0%
38.0 14
14.0%
38.01 24
24.0%
38.02 16
16.0%
38.05 19
19.0%
ValueCountFrequency (%)
38.05 19
19.0%
38.02 16
16.0%
38.01 24
24.0%
38.0 14
14.0%
37.99 15
15.0%
37.98 12
12.0%

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

2024-04-17T03:11:13.211917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:13.288198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10586
Minimum0
Maximum1.03
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T03:11:13.361008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01995
Q10.03
median0.03
Q30.03
95-th percentile1.001
Maximum1.03
Range1.03
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26837773
Coefficient of variation (CV)2.5352138
Kurtosis8.0616808
Mean0.10586
Median Absolute Deviation (MAD)0
Skewness3.1439322
Sum10.586
Variance0.072026606
MonotonicityNot monotonic
2024-04-17T03:11:13.454072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.03 66
66.0%
0.02 14
 
14.0%
1.03 4
 
4.0%
0.0 3
 
3.0%
0.029 1
 
1.0%
0.038 1
 
1.0%
0.04 1
 
1.0%
0.025 1
 
1.0%
0.022 1
 
1.0%
0.973 1
 
1.0%
Other values (7) 7
 
7.0%
ValueCountFrequency (%)
0.0 3
 
3.0%
0.016 1
 
1.0%
0.019 1
 
1.0%
0.02 14
 
14.0%
0.022 1
 
1.0%
0.024 1
 
1.0%
0.025 1
 
1.0%
0.026 1
 
1.0%
0.029 1
 
1.0%
0.03 66
66.0%
ValueCountFrequency (%)
1.03 4
 
4.0%
1.02 1
 
1.0%
1.0 1
 
1.0%
0.974 1
 
1.0%
0.973 1
 
1.0%
0.04 1
 
1.0%
0.038 1
 
1.0%
0.03 66
66.0%
0.029 1
 
1.0%
0.026 1
 
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02718
Minimum0
Maximum0.05
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T03:11:13.557176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.03
median0.03
Q30.03
95-th percentile0.03
Maximum0.05
Range0.05
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0070986981
Coefficient of variation (CV)0.26117359
Kurtosis7.4952814
Mean0.02718
Median Absolute Deviation (MAD)0
Skewness-2.1005508
Sum2.718
Variance5.0391515 × 10-5
MonotonicityNot monotonic
2024-04-17T03:11:13.644893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.03 74
74.0%
0.02 17
 
17.0%
0.0 4
 
4.0%
0.025 3
 
3.0%
0.033 1
 
1.0%
0.05 1
 
1.0%
ValueCountFrequency (%)
0.0 4
 
4.0%
0.02 17
 
17.0%
0.025 3
 
3.0%
0.03 74
74.0%
0.033 1
 
1.0%
0.05 1
 
1.0%
ValueCountFrequency (%)
0.05 1
 
1.0%
0.033 1
 
1.0%
0.03 74
74.0%
0.025 3
 
3.0%
0.02 17
 
17.0%
0.0 4
 
4.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.01132
Minimum2.006
Maximum2.018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T03:11:13.727434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.006
5-th percentile2.006
Q12.008
median2.011
Q32.013
95-th percentile2.018
Maximum2.018
Range0.012
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.0038609153
Coefficient of variation (CV)0.0019195927
Kurtosis-0.68786837
Mean2.01132
Median Absolute Deviation (MAD)0.002
Skewness0.57451505
Sum201.132
Variance1.4906667 × 10-5
MonotonicityNot monotonic
2024-04-17T03:11:13.814492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2.011 24
24.0%
2.018 19
19.0%
2.013 16
16.0%
2.008 15
15.0%
2.009 14
14.0%
2.006 12
12.0%
ValueCountFrequency (%)
2.006 12
12.0%
2.008 15
15.0%
2.009 14
14.0%
2.011 24
24.0%
2.013 16
16.0%
2.018 19
19.0%
ValueCountFrequency (%)
2.018 19
19.0%
2.013 16
16.0%
2.011 24
24.0%
2.009 14
14.0%
2.008 15
15.0%
2.006 12
12.0%

저수율
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
76.2
29 
76.3
24 
76.6
19 
76.4
16 
76.1
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row76.2
2nd row76.6
3rd row76.3
4th row76.2
5th row76.6

Common Values

ValueCountFrequency (%)
76.2 29
29.0%
76.3 24
24.0%
76.6 19
19.0%
76.4 16
16.0%
76.1 12
12.0%

Length

2024-04-17T03:11:13.935183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:14.023637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
76.2 29
29.0%
76.3 24
24.0%
76.6 19
19.0%
76.4 16
16.0%
76.1 12
12.0%

Interactions

2024-04-17T03:11:11.947053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:10.497867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:10.857885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.233232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.590735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:12.030399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:10.560291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:10.929907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.302473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.656611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:12.132176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:10.641086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.011160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.378017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.730582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:12.219636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:10.716557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.082687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.447168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.801812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:12.299661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:10.783365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.157932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.515414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:11.870798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T03:11:14.091536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8990.2360.1460.8990.818
저수위(m)0.8991.0000.0000.5971.0001.000
유입량(ms)0.2360.0001.0000.0000.0000.000
방류량(ms)0.1460.5970.0001.0000.5970.742
저수량(백만m3)0.8991.0000.0000.5971.0001.000
저수율0.8181.0000.0000.7421.0001.000
2024-04-17T03:11:14.179873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.9840.4050.5590.9840.780
저수위(m)0.9841.0000.4520.5691.0000.995
유입량(ms)0.4050.4521.0000.6730.4520.000
방류량(ms)0.5590.5690.6731.0000.5690.382
저수량(백만m3)0.9841.0000.4520.5691.0000.995
저수율0.7800.9950.0000.3820.9951.000

Missing values

2024-04-17T03:11:12.415030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T03:11:12.524458image/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
0감포20190501062037.9900.020.022.00876.2
1감포20190503044038.0500.030.032.01876.6
2감포20190501170038.0100.030.032.01176.3
3감포20190501064037.9900.020.022.00876.2
4감포20190503100038.0500.030.032.01876.6
5감포20190503050038.0500.030.032.01876.6
6감포20190501222038.0200.9740.032.01376.4
7감포20190501172038.0100.030.032.01176.3
8감포20190501120038.000.030.032.00976.2
9감포20190501070037.9900.020.022.00876.2
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90감포20190501092038.001.030.032.00976.2
91감포20190501090037.9900.030.032.00876.2
92감포20190501082037.9900.040.032.00876.2
93감포20190501080037.9900.0380.052.00876.2
94감포20190501060037.9900.020.022.00876.2
95감포20190501054037.9900.020.022.00876.2
96감포20190501050037.9900.020.022.00876.2
97감포20190501044037.9900.020.022.00876.2
98감포20190503040038.0500.030.032.01876.6
99감포20190503042038.0500.030.032.01876.6