Overview

Dataset statistics

Number of variables8
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.9 KiB
Average record size in memory67.7 B

Variable types

Categorical3
DateTime1
Numeric3
Text1

Dataset

Description한강홍수통제소에서 댐수위저수량을 구하기 위해 사용하는 곡선식입니다. 예보유역 명, 적용 시작/종료 일자, 일련번호, 관계식 등 데이터를 제공합니다.
Author환경부 한강홍수통제소
URLhttps://www.data.go.kr/data/15086305/fileData.do

Alerts

하도명 is highly overall correlated with 수위자료하한 and 2 other fieldsHigh correlation
예보유역명 is highly overall correlated with 하도명 and 1 other fieldsHigh correlation
수위자료하한 is highly overall correlated with 수위자료상한 and 1 other fieldsHigh correlation
수위자료상한 is highly overall correlated with 수위자료하한 and 1 other fieldsHigh correlation
적용종료일자 is highly overall correlated with 예보유역명High correlation

Reproduction

Analysis started2023-12-12 11:04:56.941761
Analysis finished2023-12-12 11:04:59.139537
Duration2.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

예보유역명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
낙동강
86 
한강
51 
섬진강
12 
영산강
 
8
금강
 
7
Other values (4)
16 

Length

Max length4
Median length3
Mean length2.7166667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한강
2nd row한강
3rd row한강
4th row한강
5th row한강

Common Values

ValueCountFrequency (%)
낙동강 86
47.8%
한강 51
28.3%
섬진강 12
 
6.7%
영산강 8
 
4.4%
금강 7
 
3.9%
태화강 6
 
3.3%
한강동해 5
 
2.8%
탐진강 3
 
1.7%
금강서해 2
 
1.1%

Length

2023-12-12T20:04:59.247250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:59.439462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강 86
47.8%
한강 51
28.3%
섬진강 12
 
6.7%
영산강 8
 
4.4%
금강 7
 
3.9%
태화강 6
 
3.3%
한강동해 5
 
2.8%
탐진강 3
 
1.7%
금강서해 2
 
1.1%

하도명
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2016010합천댐
18 
2019010남강댐방수로
15 
2002150임하댐
12 
1012070소양강댐
 
10
2001140안동댐
 
9
Other values (34)
116 

Length

Max length16
Median length10
Mean length10.616667
Min length7

Unique

Unique6 ?
Unique (%)3.3%

Sample

1st row1001010광동댐
2nd row1001010광동댐
3rd row1001010광동댐
4th row1001010광동댐
5th row1001010광동댐

Common Values

ValueCountFrequency (%)
2016010합천댐 18
 
10.0%
2019010남강댐방수로 15
 
8.3%
2002150임하댐 12
 
6.7%
1012070소양강댐 10
 
5.6%
2001140안동댐 9
 
5.0%
1003180충주댐 8
 
4.4%
2021060밀양댐 6
 
3.3%
2012020영천댐 6
 
3.3%
5002100 6
 
3.3%
4002010섬진강댐 6
 
3.3%
Other values (29) 84
46.7%

Length

2023-12-12T20:04:59.683669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016010합천댐 18
 
10.0%
2019010남강댐방수로 15
 
8.3%
2002150임하댐 12
 
6.7%
1012070소양강댐 10
 
5.6%
2001140안동댐 9
 
5.0%
1003180충주댐 8
 
4.4%
4002010섬진강댐 6
 
3.3%
1001010광동댐 6
 
3.3%
2021010운문댐 6
 
3.3%
1021090군남홍수조절지 6
 
3.3%
Other values (29) 84
46.7%

적용시작일자
Date

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2000-01-01 00:00:00
Maximum2018-01-01 00:00:00
2023-12-12T20:04:59.839745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:59.998587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

적용종료일자
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
9999-12-31
95 
2017-12-31
35 
2017-05-31
24 
2017-05-10
23 
2012-12-31
 
3

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-05-10
2nd row2017-05-10
3rd row2017-05-10
4th row9999-12-31
5th row9999-12-31

Common Values

ValueCountFrequency (%)
9999-12-31 95
52.8%
2017-12-31 35
 
19.4%
2017-05-31 24
 
13.3%
2017-05-10 23
 
12.8%
2012-12-31 3
 
1.7%

Length

2023-12-12T20:05:00.214826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:05:00.405966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9999-12-31 95
52.8%
2017-12-31 35
 
19.4%
2017-05-31 24
 
13.3%
2017-05-10 23
 
12.8%
2012-12-31 3
 
1.7%

일련번호
Real number (ℝ)

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3055556
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T20:05:00.572178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3499316
Coefficient of variation (CV)0.5855125
Kurtosis-0.10653059
Mean2.3055556
Median Absolute Deviation (MAD)1
Skewness0.86084632
Sum415
Variance1.8223153
MonotonicityNot monotonic
2023-12-12T20:05:00.772952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 67
37.2%
2 45
25.0%
3 33
18.3%
4 20
 
11.1%
5 11
 
6.1%
6 4
 
2.2%
ValueCountFrequency (%)
1 67
37.2%
2 45
25.0%
3 33
18.3%
4 20
 
11.1%
5 11
 
6.1%
6 4
 
2.2%
ValueCountFrequency (%)
6 4
 
2.2%
5 11
 
6.1%
4 20
 
11.1%
3 33
18.3%
2 45
25.0%
1 67
37.2%

관계식
Text

Distinct106
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T20:05:01.189403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length124
Median length71
Mean length63.322222
Min length27

Characters and Unicode

Total characters11398
Distinct characters22
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)31.1%

Sample

1st row0.025238556e-3*h^2-16.3606e-3*h-5.2770288e-17
2nd row0.028244366e-3*h^3-36.774707e-3*h^2+11970.376e-3*h-3.5697178e-9
3rd row0.040627185e-3*h^3-53.121034e-3*h^2+17364.858e-3*h-0.0026254318e-3
4th row0.025238556e-3*h^2-16.3606e-3*h-5.2770288e-17
5th row0.028244366e-3*h^3-36.774707e-3*h^2+11970.376e-3*h-3.5697178e-9
ValueCountFrequency (%)
0.0004915527*h^3-0.12314996*h^2+7.7134898*h-1.0914323e-7 3
 
1.7%
0.001623*h^3-0.58781*h^2+79.875*h-3993.2 3
 
1.7%
0.06316899767*h^3-32.59076573*h^2+5625.734035*h-324279.558 3
 
1.7%
0.0017768*h^3-0.60842*h^2+74.344*h-3214.0 3
 
1.7%
0.001069699135*h^3+0.55720820717*h^2-84.721227134598*h+4019.633418672 3
 
1.7%
0.000407925408*h^3-0.023776223776*h^2-8.736072261059*h+713.380419579899 3
 
1.7%
0.000567007*h^3-0.14229352*h^2+8.926607*h+9.1235526e-5 3
 
1.7%
0.000108461*h^4-0.067526147*h^3+16.461970867*h^2-1805.126760715*h+74116.478280092 3
 
1.7%
0.000163372*h^4-0.083253485*h^3+16.071122928*h^2-1387.037627271*h+45029.954460450 3
 
1.7%
0.000092185*h^4+0.043621462*h^3-7.600455217*h^2+580.24214158*h-16422.616366382 3
 
1.7%
Other values (96) 150
83.3%
2023-12-12T20:05:01.859610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1435
12.6%
2 999
 
8.8%
1 907
 
8.0%
3 827
 
7.3%
4 758
 
6.7%
. 740
 
6.5%
6 716
 
6.3%
5 710
 
6.2%
7 705
 
6.2%
9 668
 
5.9%
Other values (12) 2933
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8366
73.4%
Other Punctuation 1300
 
11.4%
Lowercase Letter 647
 
5.7%
Dash Punctuation 413
 
3.6%
Modifier Symbol 382
 
3.4%
Math Symbol 281
 
2.5%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1435
17.2%
2 999
11.9%
1 907
10.8%
3 827
9.9%
4 758
9.1%
6 716
8.6%
5 710
8.5%
7 705
8.4%
9 668
8.0%
8 641
7.7%
Lowercase Letter
ValueCountFrequency (%)
h 559
86.4%
e 86
 
13.3%
x 1
 
0.2%
p 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 740
56.9%
* 560
43.1%
Dash Punctuation
ValueCountFrequency (%)
- 413
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 382
100.0%
Math Symbol
ValueCountFrequency (%)
+ 281
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10750
94.3%
Latin 648
 
5.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1435
13.3%
2 999
9.3%
1 907
 
8.4%
3 827
 
7.7%
4 758
 
7.1%
. 740
 
6.9%
6 716
 
6.7%
5 710
 
6.6%
7 705
 
6.6%
9 668
 
6.2%
Other values (7) 2285
21.3%
Latin
ValueCountFrequency (%)
h 559
86.3%
e 86
 
13.3%
x 1
 
0.2%
p 1
 
0.2%
E 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1435
12.6%
2 999
 
8.8%
1 907
 
8.0%
3 827
 
7.3%
4 758
 
6.7%
. 740
 
6.5%
6 716
 
6.3%
5 710
 
6.2%
7 705
 
6.2%
9 668
 
5.9%
Other values (12) 2933
25.7%

수위자료하한
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.85206
Minimum18.7
Maximum663.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T20:05:02.091277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.7
5-th percentile28.94
Q176.375
median121
Q3150
95-th percentile262.5
Maximum663.38
Range644.68
Interquartile range (IQR)73.625

Descriptive statistics

Standard deviation123.53095
Coefficient of variation (CV)0.90930494
Kurtosis11.508415
Mean135.85206
Median Absolute Deviation (MAD)36.155
Skewness3.2666637
Sum24453.37
Variance15259.894
MonotonicityNot monotonic
2023-12-12T20:05:02.317011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110.0 5
 
2.8%
100.0 4
 
2.2%
150.0 4
 
2.2%
38.0 4
 
2.2%
121.0 3
 
1.7%
124.0 3
 
1.7%
126.0 3
 
1.7%
137.88 3
 
1.7%
150.06 3
 
1.7%
70.0 3
 
1.7%
Other values (88) 145
80.6%
ValueCountFrequency (%)
18.7 2
1.1%
20.0 2
1.1%
24.0 1
 
0.6%
28.0 3
1.7%
28.94 2
1.1%
32.0 3
1.7%
38.0 4
2.2%
40.0 1
 
0.6%
40.72 2
1.1%
42.0 3
1.7%
ValueCountFrequency (%)
663.38 2
1.1%
654.0 2
1.1%
652.69 2
1.1%
650.0 2
1.1%
310.0 1
0.6%
260.0 1
0.6%
237.03 1
0.6%
215.0 1
0.6%
205.0 1
0.6%
194.88 1
0.6%

수위자료상한
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.13383
Minimum27
Maximum711.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T20:05:02.533105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile38
Q184.2925
median130.785
Q3170.38
95-th percentile268.2935
Maximum711.5
Range684.5
Interquartile range (IQR)86.0875

Descriptive statistics

Standard deviation126.6195
Coefficient of variation (CV)0.83779721
Kurtosis10.984045
Mean151.13383
Median Absolute Deviation (MAD)46.395
Skewness3.1452252
Sum27204.09
Variance16032.499
MonotonicityNot monotonic
2023-12-12T20:05:02.715988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125.0 5
 
2.8%
50.0 5
 
2.8%
190.0 5
 
2.8%
220.0 4
 
2.2%
140.29 3
 
1.7%
126.0 3
 
1.7%
137.88 3
 
1.7%
150.06 3
 
1.7%
168.0 3
 
1.7%
70.0 3
 
1.7%
Other values (88) 143
79.4%
ValueCountFrequency (%)
27.0 2
1.1%
28.94 2
1.1%
32.0 3
1.7%
38.0 3
1.7%
40.72 2
1.1%
42.0 3
1.7%
43.67 1
 
0.6%
46.89 3
1.7%
47.8 1
 
0.6%
48.28 1
 
0.6%
ValueCountFrequency (%)
711.5 2
1.1%
680.0 2
1.1%
663.38 2
1.1%
652.69 2
1.1%
321.37 1
 
0.6%
265.5 1
 
0.6%
260.0 1
 
0.6%
237.03 1
 
0.6%
220.0 4
2.2%
215.0 1
 
0.6%

Interactions

2023-12-12T20:04:58.358526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:57.462325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:57.903723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:58.491695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:57.598845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:58.057112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:58.648445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:57.742435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:58.201375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:05:02.850967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예보유역명하도명적용시작일자적용종료일자일련번호수위자료하한수위자료상한
예보유역명1.0001.0000.8900.7070.0000.5920.578
하도명1.0001.0000.9070.7530.0000.9820.980
적용시작일자0.8900.9071.0000.7150.0000.2920.285
적용종료일자0.7070.7530.7151.0000.0000.1830.156
일련번호0.0000.0000.0000.0001.0000.4750.294
수위자료하한0.5920.9820.2920.1830.4751.0000.998
수위자료상한0.5780.9800.2850.1560.2940.9981.000
2023-12-12T20:05:03.006734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적용종료일자하도명예보유역명
적용종료일자1.0000.4170.502
하도명0.4171.0000.908
예보유역명0.5020.9081.000
2023-12-12T20:05:03.139533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호수위자료하한수위자료상한예보유역명하도명적용종료일자
일련번호1.0000.2480.2090.0000.0000.000
수위자료하한0.2481.0000.9780.3390.7980.123
수위자료상한0.2090.9781.0000.3290.7880.105
예보유역명0.0000.3390.3291.0000.9080.502
하도명0.0000.7980.7880.9081.0000.417
적용종료일자0.0000.1230.1050.5020.4171.000

Missing values

2023-12-12T20:04:58.874382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:04:59.069627image/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

예보유역명하도명적용시작일자적용종료일자일련번호관계식수위자료하한수위자료상한
0한강1001010광동댐2000-01-012017-05-1010.025238556e-3*h^2-16.3606e-3*h-5.2770288e-17650.0652.69
1한강1001010광동댐2000-01-012017-05-1020.028244366e-3*h^3-36.774707e-3*h^2+11970.376e-3*h-3.5697178e-9652.69663.38
2한강1001010광동댐2000-01-012017-05-1030.040627185e-3*h^3-53.121034e-3*h^2+17364.858e-3*h-0.0026254318e-3663.38680.0
3한강1001010광동댐2017-05-119999-12-3110.025238556e-3*h^2-16.3606e-3*h-5.2770288e-17650.0652.69
4한강1001010광동댐2017-05-119999-12-3120.028244366e-3*h^3-36.774707e-3*h^2+11970.376e-3*h-3.5697178e-9652.69663.38
5한강1001010광동댐2017-05-119999-12-3130.040627185e-3*h^3-53.121034e-3*h^2+17364.858e-3*h-0.0026254318e-3663.38680.0
6한강10010502000-01-012017-05-1011.3461749551336e-04*h^3-2.5495622345613e-01*h^2+1.6084238714004e+02*h-3.3797459406959e+04654.0711.5
7한강10010502017-05-119999-12-3111.3461749551336e-04*h^3-2.5495622345613e-01*h^2+1.6084238714004e+02*h-3.3797459406959e+04654.0711.5
8한강1003180충주댐2000-01-012017-05-1010.000114597*h^4-0.030553780*h^3+3.114702832*h^2-144.021443850*h+2548.05962139884.084.39
9한강1003180충주댐2000-01-012017-05-102-0.000013753*h^4+0.011425652*h^3-2.023349275*h^2+135.760984443*h-3191.12080001784.39110.12
예보유역명하도명적용시작일자적용종료일자일련번호관계식수위자료하한수위자료상한
170영산강50021002000-01-012012-12-3110.000241905*h^3-0.055054286*h^2+4.133738095*h-102.149285785.095.03
171영산강50021002000-01-012012-12-3120.000282667*h^3-0.0641*h^2+4.745433333*h-113.57195.03110.0
172영산강50021002000-01-012012-12-3130.000170667*h^3-0.02244*h^2-0.366466667*h+93.724110.0125.0
173영산강50021002013-01-019999-12-3110.000241905*h^3-0.055054286*h^2+4.133738095*h-102.149285785.095.03
174영산강50021002013-01-019999-12-3120.000282667*h^3-0.0641*h^2+4.745433333*h-113.57195.03110.0
175영산강50021002013-01-019999-12-3130.000170667*h^3-0.02244*h^2-0.366466667*h+93.724110.0125.0
176영산강50030152013-01-019999-12-311-0.00040571474404949*h^3+0.12868782080051*h^2-10.447010303883*h+250.7408515389360.567.6
177탐진강5101020장흥댐2000-01-019999-12-3110.3262506e-3*h^3+36.46794e-3*h^2-5183.212e-3*h+129668.8e-345.047.8
178탐진강5101020장흥댐2000-01-019999-12-3120.2438792e-3*h^3+93.86657e-3*h^2-9979.114e-3*h+236740e-347.870.0
179탐진강5101020장흥댐2000-01-019999-12-3131.19209e-3*h^3-122.8195e-3*h^2+6436.966e-3*h-175856e-370.084.0