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 18 (18.0%) zerosZeros
방류량(ms) has 37 (37.0%) zerosZeros
저수량(백만m3) has 50 (50.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:53:33.736246
Analysis finished2023-12-10 12:53:37.583083
Duration3.85 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%
Mean2.0190301 × 109
Minimum2.0190301 × 109
Maximum2.0190301 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:37.633765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190301 × 109
5-th percentile2.0190301 × 109
Q12.0190301 × 109
median2.0190301 × 109
Q32.0190301 × 109
95-th percentile2.0190301 × 109
Maximum2.0190301 × 109
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5555643
Coefficient of variation (CV)1.2657386 × 10-9
Kurtosis-1.2172581
Mean2.0190301 × 109
Median Absolute Deviation (MAD)2
Skewness0.014163202
Sum2.0190301 × 1011
Variance6.5309091
MonotonicityIncreasing
2023-12-10T21:53:37.747120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019030107 13
13.0%
2019030101 12
12.0%
2019030103 12
12.0%
2019030106 12
12.0%
2019030102 11
11.0%
2019030104 11
11.0%
2019030105 10
10.0%
2019030108 10
10.0%
2019030109 9
9.0%
ValueCountFrequency (%)
2019030101 12
12.0%
2019030102 11
11.0%
2019030103 12
12.0%
2019030104 11
11.0%
2019030105 10
10.0%
2019030106 12
12.0%
2019030107 13
13.0%
2019030108 10
10.0%
2019030109 9
9.0%
ValueCountFrequency (%)
2019030109 9
9.0%
2019030108 10
10.0%
2019030107 13
13.0%
2019030106 12
12.0%
2019030105 10
10.0%
2019030104 11
11.0%
2019030103 12
12.0%
2019030102 11
11.0%
2019030101 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

2023-12-10T21:53:37.907280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:38.000851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.75703
Minimum0
Maximum97.064
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:38.108604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.436
median13.003
Q326.08
95-th percentile76.5972
Maximum97.064
Range97.064
Interquartile range (IQR)23.644

Descriptive statistics

Standard deviation27.807464
Coefficient of variation (CV)1.1704941
Kurtosis0.81569286
Mean23.75703
Median Absolute Deviation (MAD)12.3145
Skewness1.3554444
Sum2375.703
Variance773.25507
MonotonicityNot monotonic
2023-12-10T21:53:38.516795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 18
18.0%
0.955 5
 
5.0%
97.064 5
 
5.0%
4.355 5
 
5.0%
73.509 5
 
5.0%
2.436 4
 
4.0%
14.565 4
 
4.0%
24.156 4
 
4.0%
54.803 4
 
4.0%
24.258 4
 
4.0%
Other values (27) 42
42.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.954 1
 
1.0%
0.955 5
 
5.0%
2.436 4
 
4.0%
4.324 2
 
2.0%
4.355 5
 
5.0%
6.813 1
 
1.0%
6.828 1
 
1.0%
6.842 1
 
1.0%
6.857 1
 
1.0%
ValueCountFrequency (%)
97.064 5
5.0%
75.52 4
4.0%
73.509 5
5.0%
54.803 4
4.0%
40.178 3
3.0%
40.11 2
 
2.0%
26.221 1
 
1.0%
26.08 2
 
2.0%
25.938 1
 
1.0%
25.867 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.369
Minimum0
Maximum101.6
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:38.640869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.9
median75.8
Q396.2
95-th percentile101.6
Maximum101.6
Range101.6
Interquartile range (IQR)79.3

Descriptive statistics

Standard deviation39.146556
Coefficient of variation (CV)0.69446959
Kurtosis-1.5560455
Mean56.369
Median Absolute Deviation (MAD)25.8
Skewness-0.28255727
Sum5636.9
Variance1532.4529
MonotonicityNot monotonic
2023-12-10T21:53:38.788906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.0 18
18.0%
101.6 7
 
7.0%
16.9 6
 
6.0%
96.2 5
 
5.0%
17.0 5
 
5.0%
79.6 5
 
5.0%
78.3 4
 
4.0%
15.7 4
 
4.0%
69.7 4
 
4.0%
100.4 4
 
4.0%
Other values (24) 38
38.0%
ValueCountFrequency (%)
0.0 18
18.0%
15.7 4
 
4.0%
16.8 2
 
2.0%
16.9 6
 
6.0%
17.0 5
 
5.0%
43.7 1
 
1.0%
43.8 1
 
1.0%
43.9 1
 
1.0%
44.0 1
 
1.0%
44.2 1
 
1.0%
ValueCountFrequency (%)
101.6 7
7.0%
101.5 3
3.0%
101.2 2
 
2.0%
100.5 3
3.0%
100.4 4
4.0%
100.3 4
4.0%
100.0 1
 
1.0%
96.2 5
5.0%
79.6 5
5.0%
78.3 4
4.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.18844
Minimum0
Maximum135.549
Zeros37
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:38.926758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17.87
Q332.1465
95-th percentile127.6395
Maximum135.549
Range135.549
Interquartile range (IQR)32.1465

Descriptive statistics

Standard deviation42.616864
Coefficient of variation (CV)1.4116948
Kurtosis1.2115941
Mean30.18844
Median Absolute Deviation (MAD)17.87
Skewness1.6198494
Sum3018.844
Variance1816.1971
MonotonicityNot monotonic
2023-12-10T21:53:39.124397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 37
37.0%
19.694 5
 
5.0%
3.32 2
 
2.0%
37.056 2
 
2.0%
13.75 2
 
2.0%
5.2 2
 
2.0%
21.57 1
 
1.0%
18.833 1
 
1.0%
39.389 1
 
1.0%
29.0 1
 
1.0%
Other values (46) 46
46.0%
ValueCountFrequency (%)
0.0 37
37.0%
0.807 1
 
1.0%
3.32 2
 
2.0%
5.2 2
 
2.0%
8.951 1
 
1.0%
9.311 1
 
1.0%
13.75 2
 
2.0%
17.253 1
 
1.0%
17.5 1
 
1.0%
17.773 1
 
1.0%
ValueCountFrequency (%)
135.549 1
1.0%
133.792 1
1.0%
133.207 1
1.0%
131.973 1
1.0%
130.632 1
1.0%
127.482 1
1.0%
125.68 1
1.0%
125.158 1
1.0%
124.945 1
1.0%
123.976 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.87609
Minimum0
Maximum133.792
Zeros50
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:39.292870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.66
Q329.025
95-th percentile127.56025
Maximum133.792
Range133.792
Interquartile range (IQR)29.025

Descriptive statistics

Standard deviation43.790836
Coefficient of variation (CV)1.6293604
Kurtosis1.2706193
Mean26.87609
Median Absolute Deviation (MAD)1.66
Skewness1.6769579
Sum2687.609
Variance1917.6373
MonotonicityNot monotonic
2023-12-10T21:53:39.428568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 50
50.0%
5.2 3
 
3.0%
3.32 2
 
2.0%
13.14 1
 
1.0%
32.082 1
 
1.0%
133.207 1
 
1.0%
17.507 1
 
1.0%
123.521 1
 
1.0%
129.047 1
 
1.0%
44.161 1
 
1.0%
Other values (38) 38
38.0%
ValueCountFrequency (%)
0.0 50
50.0%
3.32 2
 
2.0%
3.507 1
 
1.0%
3.617 1
 
1.0%
5.2 3
 
3.0%
13.14 1
 
1.0%
17.5 1
 
1.0%
17.507 1
 
1.0%
17.7 1
 
1.0%
17.773 1
 
1.0%
ValueCountFrequency (%)
133.792 1
1.0%
133.207 1
1.0%
131.973 1
1.0%
130.632 1
1.0%
129.047 1
1.0%
127.482 1
1.0%
125.68 1
1.0%
125.158 1
1.0%
124.945 1
1.0%
123.976 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.0879
Minimum-1.22
Maximum85.94
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)7.0%
Memory size1.0 KiB
2023-12-10T21:53:39.558276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.22
5-th percentile-1.21
Q17.7625
median25.52
Q338.06
95-th percentile81.0285
Maximum85.94
Range87.16
Interquartile range (IQR)30.2975

Descriptive statistics

Standard deviation24.457391
Coefficient of variation (CV)0.9028899
Kurtosis0.24906278
Mean27.0879
Median Absolute Deviation (MAD)17.16
Skewness0.99770198
Sum2708.79
Variance598.16399
MonotonicityNot monotonic
2023-12-10T21:53:39.694040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
85.94 5
 
5.0%
18.29 5
 
5.0%
8.36 5
 
5.0%
-1.21 5
 
5.0%
4.81 5
 
5.0%
38.06 4
 
4.0%
25.52 4
 
4.0%
9.2 4
 
4.0%
4.31 4
 
4.0%
28.05 4
 
4.0%
Other values (35) 55
55.0%
ValueCountFrequency (%)
-1.22 2
 
2.0%
-1.21 5
5.0%
4.2 1
 
1.0%
4.21 4
4.0%
4.31 4
4.0%
4.81 5
5.0%
5.97 1
 
1.0%
5.98 1
 
1.0%
5.99 1
 
1.0%
6.0 1
 
1.0%
ValueCountFrequency (%)
85.94 5
5.0%
80.77 2
 
2.0%
80.76 1
 
1.0%
80.75 1
 
1.0%
62.48 1
 
1.0%
62.47 3
3.0%
45.83 1
 
1.0%
45.81 1
 
1.0%
45.76 1
 
1.0%
45.7 1
 
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
달성보
9 
죽산보
7 
세종보
 
6
강천보
 
6
낙단보
 
5
Other values (15)
67 

Length

Max length5
Median length3
Mean length3.28
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row죽산보
2nd row안동보
3rd row구미보
4th row세종보
5th row강천보

Common Values

ValueCountFrequency (%)
달성보 9
 
9.0%
죽산보 7
 
7.0%
세종보 6
 
6.0%
강천보 6
 
6.0%
낙단보 5
 
5.0%
여주보 5
 
5.0%
창녕함안보 5
 
5.0%
구미보 5
 
5.0%
쾌쾌보 5
 
5.0%
안동보 5
 
5.0%
Other values (10) 42
42.0%

Length

2023-12-10T21:53:39.829234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
달성보 9
 
9.0%
죽산보 7
 
7.0%
세종보 6
 
6.0%
강천보 6
 
6.0%
쾌쾌보 5
 
5.0%
강정고령보 5
 
5.0%
안동보 5
 
5.0%
백제보 5
 
5.0%
구미보 5
 
5.0%
창녕함안보 5
 
5.0%
Other values (10) 42
42.0%

Interactions

2023-12-10T21:53:36.830296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:33.979371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.533317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.084504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.644320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.199873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.939560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.072799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.649095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.193629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.746183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.296538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:37.031550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.158397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.734310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.293033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.839733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.402578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:37.118663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.251073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.818568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.380248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.931975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.501926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:37.203285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.360979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.913713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.485628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.017874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.621677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:37.308679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:34.454728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.006504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:35.570066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.113825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:36.748427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:39.929174image/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)0.0001.0000.7090.6320.6610.7411.000
유입량(ms)0.0000.7091.0000.8420.8810.8161.000
방류량(ms)0.0000.6320.8421.0000.9830.5960.936
저수량(백만m3)0.0000.6610.8810.9831.0000.6440.972
저수율0.0000.7410.8160.5960.6441.0001.000
댐이름0.0001.0001.0000.9360.9721.0001.000
2023-12-10T21:53:40.055948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0310.0540.0820.051-0.0030.000
강우량(mm)0.0311.0000.6600.039-0.052-0.3330.927
유입량(ms)0.0540.6601.0000.5260.553-0.1980.923
방류량(ms)0.0820.0390.5261.0000.807-0.3150.722
저수량(백만m3)0.051-0.0520.5530.8071.000-0.4270.820
저수율-0.003-0.333-0.198-0.315-0.4271.0000.933
댐이름0.0000.9270.9230.7220.8200.9331.000

Missing values

2023-12-10T21:53:37.412420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:37.534635image/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
0201903010104.32416.817.517.5-1.22죽산보
1201903010100.00.00.00.085.94안동보
22019030101040.17876.20.00.030.78구미보
3201903010100.95516.928.96328.9638.36세종보
4201903010108.864101.6124.945124.94538.03강천보
52019030101097.06496.221.98821.9884.81창녕함안보
62019030101024.15669.713.750.038.06낙단보
72019030101025.58343.719.6940.010.24달성보
82019030101054.80378.30.00.09.2합천창녕보
9201903010100.00.00.00.062.48구담보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
90201903010800.95516.929.129.18.36세종보
91201903010900.00.00.00.085.94안동보
922019030109040.1176.10.00.030.77구미보
932019030109011.441101.5125.158125.15833.04여주보
94201903010900.00.00.00.044.49쾌쾌보
952019030109026.22144.839.4170.010.33달성보
962019030109097.06496.221.57721.5774.81창녕함안보
97201903010904.35517.017.8417.84-1.21죽산보
98201903010908.773100.5119.753119.75338.01강천보
992019030109024.17100.047.3947.394.2백제보