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 20 (20.0%) zerosZeros
유입량(ms) has 20 (20.0%) zerosZeros
방류량(ms) has 20 (20.0%) zerosZeros
저수량(백만m3) has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:53:40.995153
Analysis finished2023-12-10 12:53:45.240256
Duration4.25 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%
Mean2.0190201 × 109
Minimum2.0190201 × 109
Maximum2.0190201 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:45.281953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190201 × 109
5-th percentile2.0190201 × 109
Q12.0190201 × 109
median2.0190201 × 109
Q32.0190201 × 109
95-th percentile2.0190201 × 109
Maximum2.0190201 × 109
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3159308
Coefficient of variation (CV)1.1470568 × 10-9
Kurtosis-1.2403626
Mean2.0190201 × 109
Median Absolute Deviation (MAD)2
Skewness0.014371937
Sum2.0190201 × 1011
Variance5.3635354
MonotonicityIncreasing
2023-12-10T21:53:45.379992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2019020102 14
14.0%
2019020106 14
14.0%
2019020108 14
14.0%
2019020104 13
13.0%
2019020101 12
12.0%
2019020105 12
12.0%
2019020103 11
11.0%
2019020107 10
10.0%
ValueCountFrequency (%)
2019020101 12
12.0%
2019020102 14
14.0%
2019020103 11
11.0%
2019020104 13
13.0%
2019020105 12
12.0%
2019020106 14
14.0%
2019020107 10
10.0%
2019020108 14
14.0%
ValueCountFrequency (%)
2019020108 14
14.0%
2019020107 10
10.0%
2019020106 14
14.0%
2019020105 12
12.0%
2019020104 13
13.0%
2019020103 11
11.0%
2019020102 14
14.0%
2019020101 12
12.0%

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

2023-12-10T21:53:45.495562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:45.587571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.81192
Minimum0
Maximum97.674
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:45.679227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.443
median14.9445
Q338.40075
95-th percentile75.418
Maximum97.674
Range97.674
Interquartile range (IQR)35.95775

Descriptive statistics

Standard deviation28.68428
Coefficient of variation (CV)1.1112804
Kurtosis0.054020629
Mean25.81192
Median Absolute Deviation (MAD)14.9445
Skewness1.1089287
Sum2581.192
Variance822.78789
MonotonicityNot monotonic
2023-12-10T21:53:45.821153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0 20
20.0%
18.22 8
 
8.0%
73.656 6
 
6.0%
75.418 5
 
5.0%
9.452 4
 
4.0%
2.443 4
 
4.0%
24.258 4
 
4.0%
33.152 3
 
3.0%
27.443 3
 
3.0%
3.77 3
 
3.0%
Other values (28) 40
40.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.952 2
 
2.0%
0.953 2
 
2.0%
2.443 4
 
4.0%
3.647 1
 
1.0%
3.678 1
 
1.0%
3.77 3
 
3.0%
6.842 3
 
3.0%
6.872 2
 
2.0%
9.452 4
 
4.0%
ValueCountFrequency (%)
97.674 2
 
2.0%
97.471 1
 
1.0%
97.267 1
 
1.0%
75.418 5
5.0%
75.315 1
 
1.0%
73.656 6
6.0%
73.509 2
 
2.0%
44.166 1
 
1.0%
44.076 2
 
2.0%
43.985 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.062
Minimum0
Maximum108.3
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:46.021777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.45
median75.35
Q3100.025
95-th percentile104.115
Maximum108.3
Range108.3
Interquartile range (IQR)84.575

Descriptive statistics

Standard deviation41.905116
Coefficient of variation (CV)0.72173049
Kurtosis-1.6714401
Mean58.062
Median Absolute Deviation (MAD)27.9
Skewness-0.29621301
Sum5806.2
Variance1756.0387
MonotonicityNot monotonic
2023-12-10T21:53:46.162615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 20
20.0%
26.0 8
 
8.0%
79.8 6
 
6.0%
100.1 5
 
5.0%
108.3 4
 
4.0%
15.7 4
 
4.0%
16.9 4
 
4.0%
100.4 4
 
4.0%
95.6 3
 
3.0%
100.2 3
 
3.0%
Other values (25) 39
39.0%
ValueCountFrequency (%)
0.0 20
20.0%
14.2 1
 
1.0%
14.3 1
 
1.0%
14.7 3
 
3.0%
15.7 4
 
4.0%
16.9 4
 
4.0%
26.0 8
 
8.0%
72.3 1
 
1.0%
72.7 2
 
2.0%
73.1 2
 
2.0%
ValueCountFrequency (%)
108.3 4
4.0%
104.4 1
 
1.0%
104.1 1
 
1.0%
103.5 1
 
1.0%
103.4 2
2.0%
103.1 2
2.0%
102.7 1
 
1.0%
102.3 1
 
1.0%
100.4 4
4.0%
100.2 3
3.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5427
Minimum0
Maximum154.805
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:46.279625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.2
median31.059
Q366.2
95-th percentile135.6585
Maximum154.805
Range154.805
Interquartile range (IQR)61

Descriptive statistics

Standard deviation47.473938
Coefficient of variation (CV)0.99855367
Kurtosis-0.60128698
Mean47.5427
Median Absolute Deviation (MAD)31.059
Skewness0.86243718
Sum4754.27
Variance2253.7748
MonotonicityNot monotonic
2023-12-10T21:53:46.421095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
20.0%
66.2 6
 
6.0%
111.37 4
 
4.0%
5.2 2
 
2.0%
28.337 1
 
1.0%
131.141 1
 
1.0%
36.92 1
 
1.0%
2.923 1
 
1.0%
13.6 1
 
1.0%
150.412 1
 
1.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.878 1
 
1.0%
1.161 1
 
1.0%
2.923 1
 
1.0%
4.194 1
 
1.0%
5.2 2
 
2.0%
8.806 1
 
1.0%
9.089 1
 
1.0%
12.152 1
 
1.0%
13.2 1
 
1.0%
ValueCountFrequency (%)
154.805 1
1.0%
150.412 1
1.0%
148.2 1
1.0%
146.224 1
1.0%
139.753 1
1.0%
135.443 1
1.0%
132.067 1
1.0%
131.141 1
1.0%
129.017 1
1.0%
127.415 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.34325
Minimum0
Maximum158.613
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:46.632864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.9465
median34.6085
Q377.11675
95-th percentile139.85425
Maximum158.613
Range158.613
Interquartile range (IQR)64.17025

Descriptive statistics

Standard deviation46.77412
Coefficient of variation (CV)0.9479335
Kurtosis-0.47247047
Mean49.34325
Median Absolute Deviation (MAD)31.5915
Skewness0.83507938
Sum4934.325
Variance2187.8183
MonotonicityNot monotonic
2023-12-10T21:53:46.767623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
20.0%
66.2 6
 
6.0%
111.37 4
 
4.0%
5.2 2
 
2.0%
13.2 2
 
2.0%
39.493 1
 
1.0%
150.412 1
 
1.0%
81.598 1
 
1.0%
74.585 1
 
1.0%
36.92 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.878 1
 
1.0%
5.2 2
 
2.0%
12.75 1
 
1.0%
12.945 1
 
1.0%
12.947 1
 
1.0%
13.093 1
 
1.0%
13.2 2
 
2.0%
13.227 1
 
1.0%
13.6 1
 
1.0%
ValueCountFrequency (%)
158.613 1
1.0%
154.805 1
1.0%
152.17 1
1.0%
150.412 1
1.0%
148.2 1
1.0%
139.415 1
1.0%
135.443 1
1.0%
132.067 1
1.0%
129.017 1
1.0%
124.44 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5207
Minimum-1.44
Maximum85.96
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)5.0%
Memory size1.0 KiB
2023-12-10T21:53:46.915680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.44
5-th percentile3.9295
Q15.715
median25.51
Q340.9525
95-th percentile81.0105
Maximum85.96
Range87.4
Interquartile range (IQR)35.2375

Descriptive statistics

Standard deviation24.911645
Coefficient of variation (CV)0.87345838
Kurtosis-0.10169559
Mean28.5207
Median Absolute Deviation (MAD)19.5
Skewness0.88234073
Sum2852.07
Variance620.59003
MonotonicityNot monotonic
2023-12-10T21:53:47.060540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4.89 8
 
8.0%
62.47 7
 
7.0%
18.3 6
 
6.0%
25.51 5
 
5.0%
85.96 5
 
5.0%
38.16 4
 
4.0%
80.75 4
 
4.0%
4.21 4
 
4.0%
4.32 4
 
4.0%
5.99 3
 
3.0%
Other values (34) 50
50.0%
ValueCountFrequency (%)
-1.44 1
 
1.0%
-1.43 1
 
1.0%
-1.4 3
 
3.0%
4.21 4
4.0%
4.32 4
4.0%
4.82 1
 
1.0%
4.83 1
 
1.0%
4.84 2
 
2.0%
4.89 8
8.0%
5.99 3
 
3.0%
ValueCountFrequency (%)
85.96 5
5.0%
80.75 4
4.0%
62.47 7
7.0%
47.01 3
3.0%
47.0 2
 
2.0%
44.78 1
 
1.0%
44.76 1
 
1.0%
44.68 1
 
1.0%
44.56 1
 
1.0%
39.75 1
 
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
합천창녕보
8 
강정고령보
8 
구담보
7 
칠곡보
 
6
구미보
 
5
Other values (15)
66 

Length

Max length5
Median length3
Mean length3.4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row칠곡보
2nd row승촌보
3rd row공주보
4th row합천창녕보
5th row강정고령보

Common Values

ValueCountFrequency (%)
합천창녕보 8
 
8.0%
강정고령보 8
 
8.0%
구담보 7
 
7.0%
칠곡보 6
 
6.0%
구미보 5
 
5.0%
안동보 5
 
5.0%
죽산보 5
 
5.0%
이포보 5
 
5.0%
상주보 5
 
5.0%
달성보 5
 
5.0%
Other values (10) 41
41.0%

Length

2023-12-10T21:53:47.197348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
합천창녕보 8
 
8.0%
강정고령보 8
 
8.0%
구담보 7
 
7.0%
칠곡보 6
 
6.0%
이포보 5
 
5.0%
달성보 5
 
5.0%
상주보 5
 
5.0%
승촌보 5
 
5.0%
죽산보 5
 
5.0%
안동보 5
 
5.0%
Other values (10) 41
41.0%

Interactions

2023-12-10T21:53:44.321350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:41.262523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:41.903515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.474458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.122691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.760527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.422233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:41.366147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.043777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.596089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.241472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.873181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.511814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:41.473115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.128594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.727337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.350968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.961229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.807694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:41.561050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.205222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.855316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.435278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.045226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.893010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:41.666434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.305653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.953285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.555260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.153325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.979383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:41.792886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:42.398244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.048455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:43.664402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:44.241781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:47.285981image/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)0.0001.0000.9470.7480.8000.7731.000
유입량(ms)0.0000.9471.0000.7450.7840.8111.000
방류량(ms)0.0000.7480.7451.0000.9360.7200.960
저수량(백만m3)0.0000.8000.7840.9361.0000.7610.971
저수율0.0000.7730.8110.7200.7611.0001.000
댐이름0.0001.0001.0000.9600.9711.0001.000
2023-12-10T21:53:47.409170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.008-0.016-0.110-0.0950.0120.000
강우량(mm)-0.0081.0000.6400.5120.541-0.3430.927
유입량(ms)-0.0160.6401.0000.6560.661-0.1620.927
방류량(ms)-0.1100.5120.6561.0000.941-0.3480.641
저수량(백만m3)-0.0950.5410.6610.9411.000-0.3100.808
저수율0.012-0.343-0.162-0.348-0.3101.0000.933
댐이름0.0000.9270.9270.6410.8080.9331.000

Missing values

2023-12-10T21:53:45.093161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:45.199696image/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
02019020101075.418100.139.49339.49325.51칠곡보
1201902010106.87276.68.80612.9456.01승촌보
2201902010102.44315.734.22234.254.32공주보
32019020101018.2226.0124.44124.444.89합천창녕보
42019020101073.65679.866.266.218.3강정고령보
52019020101014.967104.4146.224158.61328.14이포보
6201902010100.00.00.00.085.96안동보
7201902010103.67814.321.50312.947-1.43죽산보
82019020101011.657103.5127.415139.41533.09여주보
9201902010100.95216.927.527.58.33세종보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
90201902010809.452108.3111.37111.3738.16강천보
912019020108027.443100.228.32728.32747.01상주보
922019020108033.15295.625.525.539.74낙단보
932019020108073.50979.628.828.818.29강정고령보
94201902010800.00.00.00.044.78쾌쾌보
952019020108024.258100.412.15236.684.21백제보
96201902010800.00.00.00.085.96안동보
97201902010800.00.00.00.062.47구담보
982019020108075.418100.154.34725.87525.51칠곡보
99201902010803.7714.721.78313.227-1.4죽산보