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 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 2 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 18 (18.0%) zerosZeros
유입량(ms) has 22 (22.0%) zerosZeros
방류량(ms) has 27 (27.0%) zerosZeros
저수량(백만m3) has 22 (22.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:50:58.396139
Analysis finished2023-12-10 12:51:02.862494
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0201201 × 109
Minimum2.0201201 × 109
Maximum2.0201201 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:51:02.935593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0201201 × 109
5-th percentile2.0201201 × 109
Q12.0201201 × 109
median2.0201201 × 109
Q32.0201201 × 109
95-th percentile2.0201201 × 109
Maximum2.0201201 × 109
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6282075
Coefficient of variation (CV)1.3010155 × 10-9
Kurtosis-1.2287958
Mean2.0201201 × 109
Median Absolute Deviation (MAD)2
Skewness0.01025767
Sum2.0201201 × 1011
Variance6.9074747
MonotonicityIncreasing
2023-12-10T21:51:03.100666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2020120101 13
13.0%
2020120103 13
13.0%
2020120105 13
13.0%
2020120107 13
13.0%
2020120109 12
12.0%
2020120102 9
9.0%
2020120104 9
9.0%
2020120106 9
9.0%
2020120108 9
9.0%
ValueCountFrequency (%)
2020120101 13
13.0%
2020120102 9
9.0%
2020120103 13
13.0%
2020120104 9
9.0%
2020120105 13
13.0%
2020120106 9
9.0%
2020120107 13
13.0%
2020120108 9
9.0%
2020120109 12
12.0%
ValueCountFrequency (%)
2020120109 12
12.0%
2020120108 9
9.0%
2020120107 13
13.0%
2020120106 9
9.0%
2020120105 13
13.0%
2020120104 9
9.0%
2020120103 13
13.0%
2020120102 9
9.0%
2020120101 13
13.0%

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

2023-12-10T21:51:03.311039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:51:03.426343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.40087
Minimum0
Maximum99.303
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:51:03.551560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.96575
median14.476
Q352.9445
95-th percentile76.55015
Maximum99.303
Range99.303
Interquartile range (IQR)51.97875

Descriptive statistics

Standard deviation28.761992
Coefficient of variation (CV)1.0894335
Kurtosis-0.18264086
Mean26.40087
Median Absolute Deviation (MAD)14.476
Skewness0.96216493
Sum2640.087
Variance827.25217
MonotonicityNot monotonic
2023-12-10T21:51:03.747478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 18
 
18.0%
8.819 5
 
5.0%
2.062 5
 
5.0%
52.994 4
 
4.0%
27.485 4
 
4.0%
50.484 4
 
4.0%
76.545 3
 
3.0%
11.398 3
 
3.0%
32.976 3
 
3.0%
74.974 3
 
3.0%
Other values (39) 48
48.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.733 1
 
1.0%
0.758 1
 
1.0%
0.779 1
 
1.0%
0.788 1
 
1.0%
0.964 1
 
1.0%
0.965 2
 
2.0%
0.966 2
 
2.0%
2.062 5
 
5.0%
6.857 1
 
1.0%
ValueCountFrequency (%)
99.303 2
2.0%
99.1 2
2.0%
76.648 1
 
1.0%
76.545 3
3.0%
74.974 3
3.0%
74.828 1
 
1.0%
55.15 1
 
1.0%
55.064 1
 
1.0%
54.977 1
 
1.0%
54.803 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.129
Minimum0
Maximum140.3
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:51:03.900705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.3
median81.2
Q3100.5
95-th percentile103.72
Maximum140.3
Range140.3
Interquartile range (IQR)87.2

Descriptive statistics

Standard deviation44.55256
Coefficient of variation (CV)0.68406638
Kurtosis-1.25787
Mean65.129
Median Absolute Deviation (MAD)19.7
Skewness-0.41430435
Sum6512.9
Variance1984.9306
MonotonicityNot monotonic
2023-12-10T21:51:04.068532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 22
22.0%
100.3 5
 
5.0%
101.0 5
 
5.0%
13.3 5
 
5.0%
17.1 5
 
5.0%
100.5 4
 
4.0%
140.2 4
 
4.0%
101.2 3
 
3.0%
95.1 3
 
3.0%
101.6 3
 
3.0%
Other values (31) 41
41.0%
ValueCountFrequency (%)
0.0 22
22.0%
13.3 5
 
5.0%
17.1 5
 
5.0%
62.4 1
 
1.0%
62.5 2
 
2.0%
62.7 2
 
2.0%
62.9 2
 
2.0%
63.2 1
 
1.0%
63.6 1
 
1.0%
76.4 1
 
1.0%
ValueCountFrequency (%)
140.3 1
 
1.0%
140.2 4
4.0%
101.8 1
 
1.0%
101.6 3
3.0%
101.2 3
3.0%
101.0 5
5.0%
100.9 2
 
2.0%
100.8 2
 
2.0%
100.6 1
 
1.0%
100.5 4
4.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.36806
Minimum0
Maximum212.276
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:51:04.221961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44.269
Q378.637
95-th percentile106.41825
Maximum212.276
Range212.276
Interquartile range (IQR)78.637

Descriptive statistics

Standard deviation41.999131
Coefficient of variation (CV)0.88665507
Kurtosis1.4132763
Mean47.36806
Median Absolute Deviation (MAD)37.2025
Skewness0.89252502
Sum4736.806
Variance1763.927
MonotonicityNot monotonic
2023-12-10T21:51:04.348597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
59.1 3
 
3.0%
0.33 2
 
2.0%
42.149 2
 
2.0%
45.38 1
 
1.0%
116.82 1
 
1.0%
119.096 1
 
1.0%
81.199 1
 
1.0%
77.873 1
 
1.0%
24.738 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 27
27.0%
0.33 2
 
2.0%
4.409 1
 
1.0%
24.738 1
 
1.0%
25.843 1
 
1.0%
26.434 1
 
1.0%
27.655 1
 
1.0%
29.315 1
 
1.0%
33.059 1
 
1.0%
34.117 1
 
1.0%
ValueCountFrequency (%)
212.276 1
1.0%
172.042 1
1.0%
119.096 1
1.0%
116.82 1
1.0%
110.584 1
1.0%
106.199 1
1.0%
102.626 1
1.0%
101.043 1
1.0%
100.376 1
1.0%
100.323 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.67869
Minimum0
Maximum110.584
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:51:04.507695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.33
median45.4025
Q375.682
95-th percentile101.4765
Maximum110.584
Range110.584
Interquartile range (IQR)75.352

Descriptive statistics

Standard deviation35.69724
Coefficient of variation (CV)0.7487043
Kurtosis-1.0872226
Mean47.67869
Median Absolute Deviation (MAD)30.954
Skewness0.054590287
Sum4767.869
Variance1274.293
MonotonicityNot monotonic
2023-12-10T21:51:04.729154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 22
 
22.0%
0.33 5
 
5.0%
59.1 3
 
3.0%
42.149 2
 
2.0%
74.21 1
 
1.0%
59.075 1
 
1.0%
97.132 1
 
1.0%
50.263 1
 
1.0%
102.626 1
 
1.0%
43.477 1
 
1.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0.0 22
22.0%
0.33 5
 
5.0%
33.27 1
 
1.0%
34.117 1
 
1.0%
34.164 1
 
1.0%
34.22 1
 
1.0%
41.772 1
 
1.0%
41.817 1
 
1.0%
41.927 1
 
1.0%
42.128 1
 
1.0%
ValueCountFrequency (%)
110.584 1
1.0%
106.707 1
1.0%
106.199 1
1.0%
105.337 1
1.0%
102.626 1
1.0%
101.416 1
1.0%
101.058 1
1.0%
101.043 1
1.0%
100.623 1
1.0%
100.376 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.3332
Minimum1.45
Maximum134.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:51:04.887325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile2.83
Q16.5975
median28.015
Q344.43
95-th percentile86.0145
Maximum134.75
Range133.3
Interquartile range (IQR)37.8325

Descriptive statistics

Standard deviation33.483182
Coefficient of variation (CV)1.0044995
Kurtosis2.1151648
Mean33.3332
Median Absolute Deviation (MAD)19.275
Skewness1.5156805
Sum3333.32
Variance1121.1235
MonotonicityNot monotonic
2023-12-10T21:51:05.068818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.02 5
 
5.0%
3.66 5
 
5.0%
44.43 5
 
5.0%
83.45 5
 
5.0%
32.54 4
 
4.0%
134.74 4
 
4.0%
80.7 4
 
4.0%
47.02 4
 
4.0%
18.39 3
 
3.0%
25.62 3
 
3.0%
Other values (45) 58
58.0%
ValueCountFrequency (%)
1.45 1
1.0%
1.47 1
1.0%
1.5 1
1.0%
1.53 1
1.0%
2.83 2
2.0%
2.84 1
1.0%
2.85 2
2.0%
3.16 1
1.0%
3.22 1
1.0%
3.27 1
1.0%
ValueCountFrequency (%)
134.75 1
 
1.0%
134.74 4
4.0%
83.45 5
5.0%
80.7 4
4.0%
68.4 1
 
1.0%
65.54 1
 
1.0%
62.51 1
 
1.0%
62.46 1
 
1.0%
47.02 4
4.0%
44.43 5
5.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
구미보
 
5
백제보
 
5
쾌쾌보
 
5
강천보
 
5
승촌보
 
5
Other values (17)
75 

Length

Max length5
Median length3
Mean length3.36
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구미보
2nd row낙단보
3rd row단양수중보
4th row강천보
5th row승촌보

Common Values

ValueCountFrequency (%)
구미보 5
 
5.0%
백제보 5
 
5.0%
쾌쾌보 5
 
5.0%
강천보 5
 
5.0%
승촌보 5
 
5.0%
공주보 5
 
5.0%
단양수중보 5
 
5.0%
합천창녕보 5
 
5.0%
안동보 5
 
5.0%
세종보 5
 
5.0%
Other values (12) 50
50.0%

Length

2023-12-10T21:51:05.266548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구미보 5
 
5.0%
합천창녕보 5
 
5.0%
백제보 5
 
5.0%
여주보 5
 
5.0%
세종보 5
 
5.0%
안동보 5
 
5.0%
낙단보 5
 
5.0%
단양수중보 5
 
5.0%
공주보 5
 
5.0%
승촌보 5
 
5.0%
Other values (12) 50
50.0%

Interactions

2023-12-10T21:51:01.703051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:58.649392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.197345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.723799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.536659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.185214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.835216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:58.744659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.291018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.852042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.630267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.286579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.921944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:58.829992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.380155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.955202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.706830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.363344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:02.024637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:58.924487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.469223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.054587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.798308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.440618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:02.127715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.012638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.559917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.130167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.970372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.515746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:02.378736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.098334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:59.636407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:00.455856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.083084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:01.602484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:51:05.371162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0000.0000.0000.0000.1380.000
강우량(mm)0.0001.0000.8830.6090.7970.7171.000
유입량(ms)0.0000.8831.0000.6340.7340.7660.994
방류량(ms)0.0000.6090.6341.0000.9110.7910.908
저수량(백만m3)0.0000.7970.7340.9111.0000.7330.971
저수율0.1380.7170.7660.7910.7331.0000.993
댐이름0.0001.0000.9940.9080.9710.9931.000
2023-12-10T21:51:05.502941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.023-0.0110.017-0.0070.0000.000
강우량(mm)-0.0231.0000.6800.4700.469-0.2270.916
유입량(ms)-0.0110.6801.0000.5440.5410.1430.886
방류량(ms)0.0170.4700.5441.0000.849-0.3560.619
저수량(백만m3)-0.0070.4690.5410.8491.000-0.4750.793
저수율0.000-0.2270.143-0.356-0.4751.0000.887
댐이름0.0000.9160.8860.6190.7930.8871.000

Missing values

2023-12-10T21:51:02.615964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:51:02.792409image/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
02020120101052.994100.568.1550.06732.54구미보
12020120101032.97695.141.81741.81739.71낙단보
22020120101050.484140.20.00.33134.74단양수중보
3202012010108.819101.097.1297.1238.02강천보
4202012010108.11290.433.05976.5876.86승촌보
5202012010102.06213.359.09559.0953.66공주보
62020120101015.20562.946.29546.2952.85백제보
7202012010100.00.00.00.044.43쾌쾌보
8202012010100.00.00.00.083.45안동보
9202012010100.96417.141.92741.9278.45세종보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
902020120109054.71678.237.89962.019.19합천창녕보
912020120109011.398101.2101.043101.04333.03여주보
922020120109015.09962.542.83542.8352.83백제보
932020120109052.928100.447.79747.79732.53구미보
94202012010908.819101.097.06797.06738.02강천보
95202012010900.00.00.00.044.43쾌쾌보
96202012010902.06213.359.159.13.66공주보
972020120109033.03495.350.49834.2239.72낙단보
982020120109050.484140.20.00.33134.74단양수중보
99202012010900.96617.144.43844.168.47세종보