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

Numeric6
Categorical2

Alerts

저수위(m) has constant value ""Constant
강우량(mm) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 댐이름High correlation
댐이름 is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
강우량(mm) has 16 (16.0%) zerosZeros
유입량(ms) has 19 (19.0%) zerosZeros
방류량(ms) has 16 (16.0%) zerosZeros
저수량(백만m3) has 16 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:52:11.523006
Analysis finished2023-12-10 12:52:15.891506
Duration4.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200201 × 109
Minimum2.0200201 × 109
Maximum2.0200201 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:15.954158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200201 × 109
5-th percentile2.0200201 × 109
Q12.0200201 × 109
median2.0200201 × 109
Q32.0200201 × 109
95-th percentile2.0200201 × 109
Maximum2.0200201 × 109
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9982568
Coefficient of variation (CV)9.892262 × 10-10
Kurtosis-1.2538449
Mean2.0200201 × 109
Median Absolute Deviation (MAD)2
Skewness-0.05863434
Sum2.0200201 × 1011
Variance3.9930303
MonotonicityIncreasing
2023-12-10T21:52:16.115927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2020020106 20
20.0%
2020020102 18
18.0%
2020020104 16
16.0%
2020020105 12
12.0%
2020020107 12
12.0%
2020020101 11
11.0%
2020020103 10
10.0%
2020020108 1
 
1.0%
ValueCountFrequency (%)
2020020101 11
11.0%
2020020102 18
18.0%
2020020103 10
10.0%
2020020104 16
16.0%
2020020105 12
12.0%
2020020106 20
20.0%
2020020107 12
12.0%
2020020108 1
 
1.0%
ValueCountFrequency (%)
2020020108 1
 
1.0%
2020020107 12
12.0%
2020020106 20
20.0%
2020020105 12
12.0%
2020020104 16
16.0%
2020020103 10
10.0%
2020020102 18
18.0%
2020020101 11
11.0%

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

2023-12-10T21:52:16.258365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:52:16.359263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.47772
Minimum0
Maximum98.489
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:16.501624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.463
median16.259
Q351.3685
95-th percentile77.83695
Maximum98.489
Range98.489
Interquartile range (IQR)48.9055

Descriptive statistics

Standard deviation28.273149
Coefficient of variation (CV)1.0289481
Kurtosis0.13898744
Mean27.47772
Median Absolute Deviation (MAD)15.5035
Skewness1.0388574
Sum2747.772
Variance799.37096
MonotonicityNot monotonic
2023-12-10T21:52:16.641591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 16
16.0%
34.032 7
 
7.0%
14.521 7
 
7.0%
6.961 5
 
5.0%
2.463 5
 
5.0%
27.612 5
 
5.0%
98.489 5
 
5.0%
9.181 4
 
4.0%
74.535 4
 
4.0%
54.748 3
 
3.0%
Other values (22) 39
39.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.749 1
 
1.0%
0.762 1
 
1.0%
0.766 1
 
1.0%
0.966 1
 
1.0%
0.967 2
 
2.0%
2.463 5
 
5.0%
6.961 5
 
5.0%
6.976 1
 
1.0%
9.181 4
 
4.0%
ValueCountFrequency (%)
98.489 5
5.0%
76.75 3
3.0%
76.648 1
 
1.0%
74.535 4
4.0%
54.855 2
 
2.0%
54.748 3
3.0%
53.775 3
3.0%
53.71 1
 
1.0%
51.412 3
3.0%
51.354 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.307
Minimum0
Maximum142.7
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:16.754466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.1
median93.6
Q3101.225
95-th percentile105.2
Maximum142.7
Range142.7
Interquartile range (IQR)84.125

Descriptive statistics

Standard deviation43.314278
Coefficient of variation (CV)0.62496254
Kurtosis-1.0666107
Mean69.307
Median Absolute Deviation (MAD)12.9
Skewness-0.57675206
Sum6930.7
Variance1876.1267
MonotonicityNot monotonic
2023-12-10T21:52:16.879398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 19
19.0%
101.3 7
 
7.0%
98.1 7
 
7.0%
97.6 5
 
5.0%
15.9 5
 
5.0%
100.8 5
 
5.0%
77.6 5
 
5.0%
101.9 4
 
4.0%
105.2 4
 
4.0%
80.7 4
 
4.0%
Other values (17) 35
35.0%
ValueCountFrequency (%)
0.0 19
19.0%
15.9 5
 
5.0%
17.1 3
 
3.0%
45.0 1
 
1.0%
45.1 3
 
3.0%
45.3 1
 
1.0%
63.3 3
 
3.0%
63.5 2
 
2.0%
77.6 5
 
5.0%
77.8 1
 
1.0%
ValueCountFrequency (%)
142.7 3
3.0%
142.6 1
 
1.0%
105.2 4
4.0%
102.0 3
3.0%
101.9 4
4.0%
101.8 1
 
1.0%
101.5 2
 
2.0%
101.3 7
7.0%
101.2 2
 
2.0%
100.8 5
5.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.36658
Minimum0
Maximum149.503
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:17.008703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.2
median45.01
Q394.50325
95-th percentile116.37765
Maximum149.503
Range149.503
Interquartile range (IQR)81.30325

Descriptive statistics

Standard deviation43.581027
Coefficient of variation (CV)0.80161429
Kurtosis-0.82265968
Mean54.36658
Median Absolute Deviation (MAD)40.2405
Skewness0.42839216
Sum5436.658
Variance1899.3059
MonotonicityNot monotonic
2023-12-10T21:52:17.154145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
13.2 5
 
5.0%
21.0 4
 
4.0%
52.445 3
 
3.0%
96.374 3
 
3.0%
41.0 2
 
2.0%
0.33 2
 
2.0%
16.608 2
 
2.0%
42.5 2
 
2.0%
112.005 2
 
2.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.33 2
 
2.0%
5.457 1
 
1.0%
8.512 1
 
1.0%
9.089 1
 
1.0%
12.876 1
 
1.0%
13.2 5
 
5.0%
16.608 2
 
2.0%
21.0 4
 
4.0%
32.389 1
 
1.0%
ValueCountFrequency (%)
149.503 1
1.0%
149.353 1
1.0%
149.322 1
1.0%
149.233 1
1.0%
149.203 1
1.0%
114.65 1
1.0%
114.59 1
1.0%
113.438 1
1.0%
112.073 1
1.0%
112.01 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.33493
Minimum0
Maximum149.503
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:17.549966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.2
median43.22
Q392.47575
95-th percentile115.22625
Maximum149.503
Range149.503
Interquartile range (IQR)79.27575

Descriptive statistics

Standard deviation43.218417
Coefficient of variation (CV)0.82580443
Kurtosis-0.69103478
Mean52.33493
Median Absolute Deviation (MAD)37.8265
Skewness0.51563573
Sum5233.493
Variance1867.8316
MonotonicityNot monotonic
2023-12-10T21:52:17.721357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
13.2 6
 
6.0%
21.0 5
 
5.0%
0.33 4
 
4.0%
52.445 3
 
3.0%
96.374 3
 
3.0%
41.0 2
 
2.0%
42.5 2
 
2.0%
112.005 2
 
2.0%
38.964 1
 
1.0%
Other values (56) 56
56.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.33 4
 
4.0%
10.457 1
 
1.0%
10.654 1
 
1.0%
10.735 1
 
1.0%
13.2 6
 
6.0%
21.0 5
 
5.0%
37.3 1
 
1.0%
38.964 1
 
1.0%
39.197 1
 
1.0%
ValueCountFrequency (%)
149.503 1
1.0%
149.353 1
1.0%
149.322 1
1.0%
149.233 1
1.0%
149.203 1
1.0%
113.438 1
1.0%
112.073 1
1.0%
112.01 1
1.0%
112.005 2
2.0%
111.98 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.5252
Minimum1.51
Maximum134.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:17.894713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.51
5-th percentile3.116
Q16.07
median28.04
Q341.025
95-th percentile85.88
Maximum134.84
Range133.33
Interquartile range (IQR)34.955

Descriptive statistics

Standard deviation31.339456
Coefficient of variation (CV)0.99410807
Kurtosis2.8317772
Mean31.5252
Median Absolute Deviation (MAD)19.56
Skewness1.6275989
Sum3152.52
Variance982.16148
MonotonicityNot monotonic
2023-12-10T21:52:18.052759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
39.89 7
 
7.0%
28.04 7
 
7.0%
4.35 5
 
5.0%
47.05 5
 
5.0%
6.07 5
 
5.0%
44.43 5
 
5.0%
4.88 5
 
5.0%
38.1 4
 
4.0%
18.36 4
 
4.0%
1.51 3
 
3.0%
Other values (29) 50
50.0%
ValueCountFrequency (%)
1.51 3
3.0%
1.52 2
 
2.0%
3.2 1
 
1.0%
3.23 1
 
1.0%
3.24 1
 
1.0%
4.2 2
 
2.0%
4.21 2
 
2.0%
4.35 5
5.0%
4.88 5
5.0%
6.07 5
5.0%
ValueCountFrequency (%)
134.84 3
3.0%
134.83 1
 
1.0%
85.88 3
3.0%
80.23 1
 
1.0%
80.22 1
 
1.0%
80.18 1
 
1.0%
80.17 1
 
1.0%
80.16 1
 
1.0%
62.47 3
3.0%
47.05 5
5.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
낙단보
 
7
이포보
 
7
승촌보
 
6
수하보
 
5
합천창녕보
 
5
Other values (17)
70 

Length

Max length5
Median length3
Mean length3.36
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row칠곡보
2nd row상주보
3rd row창녕함안보
4th row이포보
5th row죽산보

Common Values

ValueCountFrequency (%)
낙단보 7
 
7.0%
이포보 7
 
7.0%
승촌보 6
 
6.0%
수하보 5
 
5.0%
합천창녕보 5
 
5.0%
쾌쾌보 5
 
5.0%
상주보 5
 
5.0%
공주보 5
 
5.0%
달성보 5
 
5.0%
죽산보 5
 
5.0%
Other values (12) 45
45.0%

Length

2023-12-10T21:52:18.197860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙단보 7
 
7.0%
이포보 7
 
7.0%
승촌보 6
 
6.0%
수하보 5
 
5.0%
합천창녕보 5
 
5.0%
쾌쾌보 5
 
5.0%
상주보 5
 
5.0%
공주보 5
 
5.0%
달성보 5
 
5.0%
죽산보 5
 
5.0%
Other values (12) 45
45.0%

Interactions

2023-12-10T21:52:15.067879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.008253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.659612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.157523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.867911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.439295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:15.156388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.110399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.741472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.292916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.958693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.569032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:15.235755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.206543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.818115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.398500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.050285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.673631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:15.326921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.355714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.896776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.513036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.146322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.762840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:15.421337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.477915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.980669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.632862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.237179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.850224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:15.539680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:12.574743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.063991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:13.756901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.326696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:14.954283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:52:18.279105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0000.0000.0000.0000.0000.000
강우량(mm)0.0001.0000.7960.8340.8700.6761.000
유입량(ms)0.0000.7961.0000.7970.9380.8961.000
방류량(ms)0.0000.8340.7971.0000.9640.6540.965
저수량(백만m3)0.0000.8700.9380.9641.0000.8380.998
저수율0.0000.6760.8960.6540.8381.0001.000
댐이름0.0001.0001.0000.9650.9981.0001.000
2023-12-10T21:52:18.397002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.116-0.122-0.104-0.0710.0040.000
강우량(mm)-0.1161.0000.6130.6050.566-0.1860.916
유입량(ms)-0.1220.6131.0000.5990.5920.1410.921
방류량(ms)-0.1040.6050.5991.0000.982-0.2460.770
저수량(백만m3)-0.0710.5660.5920.9821.000-0.2380.913
저수율0.004-0.1860.141-0.246-0.2381.0000.921
댐이름0.0000.9160.9210.7700.9130.9211.000

Missing values

2023-12-10T21:52:15.689522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:52:15.832758image/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

일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
02020020101076.648101.867.43638.96425.63칠곡보
12020020101027.612100.853.5653.5647.05상주보
22020020101098.48997.6149.233149.2334.88창녕함안보
32020020101014.521101.3113.438113.43828.04이포보
42020020101016.25963.321.021.01.51죽산보
52020020101054.85593.798.12798.12713.65달성보
62020020101051.354142.616.6080.33134.83단양수중보
72020020101034.03298.168.50952.23139.89낙단보
8202002010106.96177.613.213.26.07승촌보
9202002010100.00.00.00.080.23수하보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
902020020107054.74893.596.37496.37413.64달성보
912020020107027.612100.853.55353.55347.05상주보
922020020107016.363.521.021.01.52죽산보
932020020107024.258100.446.3246.324.21백제보
94202002010700.00.00.00.080.16수하보
952020020107014.521101.3112.01112.0128.04이포보
96202002010706.96177.69.08913.26.07승촌보
97202002010702.46315.939.36239.3624.35공주보
982020020107053.775102.074.13774.13732.66구미보
99202002010800.00.00.00.044.43쾌쾌보