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 21 (21.0%) zerosZeros
유입량(ms) has 21 (21.0%) zerosZeros
방류량(ms) has 23 (23.0%) zerosZeros
저수량(백만m3) has 21 (21.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:53:11.407599
Analysis finished2023-12-10 12:53:15.381919
Duration3.97 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%
Mean2.0190601 × 109
Minimum2.0190601 × 109
Maximum2.0190601 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:15.432679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190601 × 109
5-th percentile2.0190601 × 109
Q12.0190601 × 109
median2.0190601 × 109
Q32.0190601 × 109
95-th percentile2.0190601 × 109
Maximum2.0190601 × 109
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4498815
Coefficient of variation (CV)1.2133772 × 10-9
Kurtosis-1.1880612
Mean2.0190601 × 109
Median Absolute Deviation (MAD)2
Skewness0.04678735
Sum2.0190601 × 1011
Variance6.0019192
MonotonicityIncreasing
2023-12-10T21:53:15.837107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019060101 15
15.0%
2019060107 15
15.0%
2019060103 14
14.0%
2019060105 13
13.0%
2019060104 11
11.0%
2019060108 10
10.0%
2019060102 9
9.0%
2019060106 9
9.0%
2019060109 4
 
4.0%
ValueCountFrequency (%)
2019060101 15
15.0%
2019060102 9
9.0%
2019060103 14
14.0%
2019060104 11
11.0%
2019060105 13
13.0%
2019060106 9
9.0%
2019060107 15
15.0%
2019060108 10
10.0%
2019060109 4
 
4.0%
ValueCountFrequency (%)
2019060109 4
 
4.0%
2019060108 10
10.0%
2019060107 15
15.0%
2019060106 9
9.0%
2019060105 13
13.0%
2019060104 11
11.0%
2019060103 14
14.0%
2019060102 9
9.0%
2019060101 15
15.0%

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

2023-12-10T21:53:15.976974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:16.090929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.31909
Minimum0
Maximum97.471
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:16.207991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.0755
median16.4025
Q354.21975
95-th percentile80.68285
Maximum97.471
Range97.471
Interquartile range (IQR)52.14425

Descriptive statistics

Standard deviation30.951763
Coefficient of variation (CV)1.0556864
Kurtosis-0.67798015
Mean29.31909
Median Absolute Deviation (MAD)16.4025
Skewness0.81688485
Sum2931.909
Variance958.01166
MonotonicityNot monotonic
2023-12-10T21:53:16.371003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 21
21.0%
2.443 6
 
6.0%
75.725 5
 
5.0%
33.738 5
 
5.0%
8.819 5
 
5.0%
97.471 4
 
4.0%
11.441 3
 
3.0%
27.443 3
 
3.0%
0.972 3
 
3.0%
24.258 3
 
3.0%
Other values (31) 42
42.0%
ValueCountFrequency (%)
0.0 21
21.0%
0.972 3
 
3.0%
0.973 1
 
1.0%
2.443 6
 
6.0%
6.31 1
 
1.0%
6.325 1
 
1.0%
6.34 1
 
1.0%
6.354 2
 
2.0%
8.819 5
 
5.0%
11.398 1
 
1.0%
ValueCountFrequency (%)
97.471 4
4.0%
97.267 1
 
1.0%
79.81 2
 
2.0%
79.664 3
3.0%
79.517 1
 
1.0%
75.725 5
5.0%
67.649 1
 
1.0%
67.505 1
 
1.0%
67.217 1
 
1.0%
67.072 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.543
Minimum0
Maximum101.5
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:16.542949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.7
median96.55
Q3100.325
95-th percentile101
Maximum101.5
Range101.5
Interquartile range (IQR)84.625

Descriptive statistics

Standard deviation42.516638
Coefficient of variation (CV)0.63893479
Kurtosis-1.3194706
Mean66.543
Median Absolute Deviation (MAD)4.55
Skewness-0.7360404
Sum6654.3
Variance1807.6645
MonotonicityNot monotonic
2023-12-10T21:53:16.718123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 21
21.0%
15.7 6
 
6.0%
97.3 5
 
5.0%
100.5 5
 
5.0%
101.0 5
 
5.0%
96.6 4
 
4.0%
17.2 4
 
4.0%
101.5 3
 
3.0%
100.9 3
 
3.0%
100.4 3
 
3.0%
Other values (29) 41
41.0%
ValueCountFrequency (%)
0.0 21
21.0%
15.7 6
 
6.0%
17.2 4
 
4.0%
63.8 1
 
1.0%
63.9 2
 
2.0%
64.1 1
 
1.0%
70.3 1
 
1.0%
70.5 1
 
1.0%
70.7 1
 
1.0%
70.8 2
 
2.0%
ValueCountFrequency (%)
101.5 3
3.0%
101.2 1
 
1.0%
101.0 5
5.0%
100.9 3
3.0%
100.7 2
 
2.0%
100.6 3
3.0%
100.5 5
5.0%
100.4 3
3.0%
100.3 1
 
1.0%
100.2 3
3.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.50407
Minimum0
Maximum110.038
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:16.865901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.6
median47.144
Q365.0645
95-th percentile93.6779
Maximum110.038
Range110.038
Interquartile range (IQR)52.4645

Descriptive statistics

Standard deviation31.25595
Coefficient of variation (CV)0.75308156
Kurtosis-1.073635
Mean41.50407
Median Absolute Deviation (MAD)26.973
Skewness0.092110034
Sum4150.407
Variance976.93441
MonotonicityNot monotonic
2023-12-10T21:53:16.996436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
23.0%
12.6 3
 
3.0%
16.711 2
 
2.0%
21.0 2
 
2.0%
48.865 2
 
2.0%
33.335 1
 
1.0%
34.688 1
 
1.0%
45.508 1
 
1.0%
76.901 1
 
1.0%
50.197 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
0.0 23
23.0%
9.561 1
 
1.0%
12.6 3
 
3.0%
14.052 1
 
1.0%
14.08 1
 
1.0%
16.711 2
 
2.0%
17.159 1
 
1.0%
20.136 1
 
1.0%
20.965 1
 
1.0%
21.0 2
 
2.0%
ValueCountFrequency (%)
110.038 1
1.0%
97.945 1
1.0%
97.074 1
1.0%
96.024 1
1.0%
94.151 1
1.0%
93.653 1
1.0%
93.505 1
1.0%
86.493 1
1.0%
82.807 1
1.0%
81.932 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.24816
Minimum0
Maximum145.437
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:17.132271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.33
median48.2475
Q361.2055
95-th percentile81.97575
Maximum145.437
Range145.437
Interquartile range (IQR)60.8755

Descriptive statistics

Standard deviation30.912024
Coefficient of variation (CV)0.76803571
Kurtosis-0.18431873
Mean40.24816
Median Absolute Deviation (MAD)25.651
Skewness0.23228151
Sum4024.816
Variance955.55324
MonotonicityNot monotonic
2023-12-10T21:53:17.299023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
21.0%
0.33 5
 
5.0%
12.6 5
 
5.0%
21.0 2
 
2.0%
48.865 2
 
2.0%
33.335 1
 
1.0%
34.688 1
 
1.0%
45.508 1
 
1.0%
76.901 1
 
1.0%
50.225 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 21
21.0%
0.33 5
 
5.0%
12.6 5
 
5.0%
20.922 1
 
1.0%
20.965 1
 
1.0%
21.0 2
 
2.0%
33.327 1
 
1.0%
33.335 1
 
1.0%
33.34 1
 
1.0%
34.688 1
 
1.0%
ValueCountFrequency (%)
145.437 1
1.0%
93.653 1
1.0%
93.505 1
1.0%
85.556 1
1.0%
82.807 1
1.0%
81.932 1
1.0%
81.925 1
1.0%
81.912 1
1.0%
81.897 1
1.0%
81.822 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.8974
Minimum1.54
Maximum131.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:17.470092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile4.2095
Q18.53
median28.03
Q347.84
95-th percentile88.057
Maximum131.99
Range130.45
Interquartile range (IQR)39.31

Descriptive statistics

Standard deviation33.568753
Coefficient of variation (CV)0.93513048
Kurtosis1.2602552
Mean35.8974
Median Absolute Deviation (MAD)19.655
Skewness1.2920801
Sum3589.74
Variance1126.8612
MonotonicityNot monotonic
2023-12-10T21:53:17.673073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
80.61 6
 
6.0%
4.32 6
 
6.0%
39.84 5
 
5.0%
25.54 5
 
5.0%
38.02 5
 
5.0%
62.52 4
 
4.0%
4.83 4
 
4.0%
85.75 4
 
4.0%
33.04 3
 
3.0%
28.03 3
 
3.0%
Other values (37) 55
55.0%
ValueCountFrequency (%)
1.54 1
 
1.0%
1.55 2
 
2.0%
1.56 1
 
1.0%
4.2 1
 
1.0%
4.21 3
3.0%
4.32 6
6.0%
4.82 1
 
1.0%
4.83 4
4.0%
5.63 1
 
1.0%
5.64 1
 
1.0%
ValueCountFrequency (%)
131.99 1
 
1.0%
131.98 1
 
1.0%
131.95 1
 
1.0%
131.94 1
 
1.0%
131.89 1
 
1.0%
85.75 4
4.0%
80.62 2
 
2.0%
80.61 6
6.0%
62.53 1
 
1.0%
62.52 4
4.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
수하보
8 
강정고령보
 
6
공주보
 
6
칠곡보
 
5
구담보
 
5
Other values (16)
70 

Length

Max length5
Median length3
Mean length3.4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row죽산보
2nd row강정고령보
3rd row합천창녕보
4th row수하보
5th row승촌보

Common Values

ValueCountFrequency (%)
수하보 8
 
8.0%
강정고령보 6
 
6.0%
공주보 6
 
6.0%
칠곡보 5
 
5.0%
구담보 5
 
5.0%
강천보 5
 
5.0%
달성보 5
 
5.0%
창녕함안보 5
 
5.0%
낙단보 5
 
5.0%
승촌보 5
 
5.0%
Other values (11) 45
45.0%

Length

2023-12-10T21:53:17.831603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수하보 8
 
8.0%
공주보 6
 
6.0%
강정고령보 6
 
6.0%
창녕함안보 5
 
5.0%
승촌보 5
 
5.0%
낙단보 5
 
5.0%
단양수중보 5
 
5.0%
달성보 5
 
5.0%
강천보 5
 
5.0%
구담보 5
 
5.0%
Other values (11) 45
45.0%

Interactions

2023-12-10T21:53:14.578748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:11.667541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.285295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.875226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.458153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.969795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.688771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:11.781223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.417246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.977264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.543057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.087776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.785441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:11.875007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.494201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.057211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.628558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.171869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.880909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:11.969679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.595416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.161575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.708200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.252620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.958751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.076231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.678184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.247398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.786714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.392665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:15.048869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.185394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:12.784580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.374902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:13.871528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:14.497332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:17.924364image/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)0.0001.0000.8120.6840.7700.7791.000
유입량(ms)0.0000.8121.0000.9330.7340.7601.000
방류량(ms)0.0000.6840.9331.0000.8990.6410.902
저수량(백만m3)0.0000.7700.7340.8991.0000.6430.959
저수율0.0000.7790.7600.6410.6431.0001.000
댐이름0.0001.0001.0000.9020.9591.0001.000
2023-12-10T21:53:18.031207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.030-0.070-0.039-0.0570.0900.000
강우량(mm)-0.0301.0000.5790.5370.541-0.3590.932
유입량(ms)-0.0700.5791.0000.6790.719-0.1170.912
방류량(ms)-0.0390.5370.6791.0000.873-0.4010.583
저수량(백만m3)-0.0570.5410.7190.8731.000-0.4700.764
저수율0.090-0.359-0.117-0.401-0.4701.0000.922
댐이름0.0000.9320.9120.5830.7640.9221.000

Missing values

2023-12-10T21:53:15.187741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:15.326314image/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
02019060101016.38263.89.56120.9221.54죽산보
12019060101079.51786.153.40553.40518.7강정고령보
22019060101067.07295.973.38333.32710.3합천창녕보
3201906010100.00.00.00.080.62수하보
4201906010106.3170.316.71112.65.63승촌보
52019060101033.73897.353.86553.86539.84낙단보
6201906010102.44315.750.87250.94.32공주보
72019060101024.258100.466.21166.2114.21백제보
82019060101097.47196.632.326145.4374.83창녕함안보
92019060101075.725100.548.86548.86525.54칠곡보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
90201906010800.00.00.00.080.61수하보
912019060108075.725100.548.8848.8825.54칠곡보
92201906010800.00.00.00.085.75안동보
93201906010800.00.00.00.047.84쾌쾌보
942019060108035.92399.70.00.33131.98단양수중보
95201906010802.44315.749.00649.0334.32공주보
962019060109079.8186.457.19357.19318.72강정고령보
972019060109097.47196.673.06773.0674.83창녕함안보
982019060109057.71998.665.0565.0513.92달성보
99201906010900.00.00.00.080.61수하보