Overview

Dataset statistics

Number of variables9
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory77.6 B

Variable types

Categorical5
DateTime1
Text1
Numeric2

Dataset

Description한강홍수통제소에서 여수로 수위 방류량을 계산하기 위해 사용하는 곡선식입니다. 예보유역명, 적용시작/종료일자, 일련번호, 관계식 등 데이터를 제공합니다.
Author환경부 한강홍수통제소
URLhttps://www.data.go.kr/data/15086303/fileData.do

Alerts

수위자료하한 is highly overall correlated with 수위자료상한 and 1 other fieldsHigh correlation
수위자료상한 is highly overall correlated with 수위자료하한 and 1 other fieldsHigh correlation
예보유역명 is highly overall correlated with 하도명 and 1 other fieldsHigh correlation
하도명 is highly overall correlated with 수위자료하한 and 2 other fieldsHigh correlation
일련번호 is highly overall correlated with 예보유역명High correlation
일련번호 is highly imbalanced (90.5%)Imbalance

Reproduction

Analysis started2023-12-11 23:26:40.677064
Analysis finished2023-12-11 23:26:41.715055
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

예보유역명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
낙동강
37 
한강
32 
태화강
 
3
한강동해
 
2
금강
 
2
Other values (4)
6 

Length

Max length4
Median length3
Mean length2.6219512
Min length2

Unique

Unique2 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강 37
45.1%
한강 32
39.0%
태화강 3
 
3.7%
한강동해 2
 
2.4%
금강 2
 
2.4%
섬진강 2
 
2.4%
영산강 2
 
2.4%
금강서해 1
 
1.2%
탐진강 1
 
1.2%

Length

2023-12-12T08:26:41.790177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:26:41.934157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강 37
45.1%
한강 32
39.0%
태화강 3
 
3.7%
한강동해 2
 
2.4%
금강 2
 
2.4%
섬진강 2
 
2.4%
영산강 2
 
2.4%
금강서해 1
 
1.2%
탐진강 1
 
1.2%

하도명
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
1021090군남홍수조절지
6 
2019010남강댐방수로
6 
2012020영천댐
6 
2021010운문댐
6 
1001010광동댐
 
4
Other values (31)
54 

Length

Max length16
Median length10
Mean length10.926829
Min length7

Unique

Unique14 ?
Unique (%)17.1%

Sample

1st row1001010광동댐
2nd row1001010광동댐
3rd row1001010광동댐
4th row1001010광동댐
5th row1001050

Common Values

ValueCountFrequency (%)
1021090군남홍수조절지 6
 
7.3%
2019010남강댐방수로 6
 
7.3%
2012020영천댐 6
 
7.3%
2021010운문댐 6
 
7.3%
1001010광동댐 4
 
4.9%
1001050 4
 
4.9%
2021060밀양댐 3
 
3.7%
2016010합천댐 3
 
3.7%
2001140안동댐 3
 
3.7%
2002150임하댐 3
 
3.7%
Other values (26) 38
46.3%

Length

2023-12-12T08:26:42.069417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1021090군남홍수조절지 6
 
7.3%
2012020영천댐 6
 
7.3%
2021010운문댐 6
 
7.3%
2019010남강댐방수로 6
 
7.3%
1001010광동댐 4
 
4.9%
1001050 4
 
4.9%
2021060밀양댐 3
 
3.7%
2016010합천댐 3
 
3.7%
2001140안동댐 3
 
3.7%
2002150임하댐 3
 
3.7%
Other values (26) 38
46.3%

적용시작일자
Date

Distinct6
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size788.0 B
Minimum2000-01-01 00:00:00
Maximum2018-01-01 00:00:00
2023-12-12T08:26:42.160521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:42.251493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

적용종료일자
Categorical

Distinct4
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
9999-12-31
42 
2017-05-10
16 
2017-12-31
14 
2017-05-31
10 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
9999-12-31 42
51.2%
2017-05-10 16
 
19.5%
2017-12-31 14
 
17.1%
2017-05-31 10
 
12.2%

Length

2023-12-12T08:26:42.353230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:26:42.452368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9999-12-31 42
51.2%
2017-05-10 16
 
19.5%
2017-12-31 14
 
17.1%
2017-05-31 10
 
12.2%

여수로일련번호
Categorical

Distinct3
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size788.0 B
1
64 
2
16 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 64
78.0%
2 16
 
19.5%
3 2
 
2.4%

Length

2023-12-12T08:26:42.622497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:26:42.741913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 64
78.0%
2 16
 
19.5%
3 2
 
2.4%

일련번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
1
81 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 81
98.8%
2 1
 
1.2%

Length

2023-12-12T08:26:42.862205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:26:42.972264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 81
98.8%
2 1
 
1.2%

관계식
Text

Distinct46
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T08:26:43.167985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length137
Median length118
Mean length87.292683
Min length52

Characters and Unicode

Total characters7158
Distinct characters19
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)24.4%

Sample

1st row-0.12774970873267*h^3+260.42408281241*h^2-176884.75491648*h+40030894.607591
2nd row-0.49179445789605*h^3+1002.1657809501*h^2-680642.82137166*h+154072077.27935
3rd row-0.12774970873267*h^3+260.42408281241*h^2-176884.75491648*h+40030894.607591
4th row-0.49179445789605*h^3+1002.1657809501*h^2-680642.82137166*h+154072077.27935
5th row-0.10966072266373*h^3+235.00898736051*h^2-167733.61477186*h+39872994.399615
ValueCountFrequency (%)
0.099020155703386*h^3+65.784226165624*h^2-14406.781773243*h+1042156.0018816 3
 
3.7%
0.16092785028292*h^3+88.132487646047*h^2-15938.069126725*h+953270.001779 3
 
3.7%
15.517948718509*h^5+12278.258741704*h^4-3885904.6003745*h^3+614908481.80044*h^2-48651016958.857*h+1539666034721.9 3
 
3.7%
0.0076081166206166*h^6+6.6721374661107*h^5-2436.8802198552*h^4+474458.5425275*h^3-51937907.298234*h^2+3030903985.1384*h-73663888350.13 3
 
3.7%
3.2167672144791e-02*h^3-4.3496303010497e+00*h^2+2.0726567981222e+02*h-3.1447874932399e+03 3
 
3.7%
0.096886099746513*h^3+11.752308967412*h^2-428.20308649313*h+4878.9489104844 3
 
3.7%
0.085129235064954*h^3+44.614607125125*h^2-7632.0487946869*h+428263.34655346 3
 
3.7%
0.097879726053066*h^3+50.889316204935*h^2-8654.3198390336*h+483469.67723038 3
 
3.7%
0.087597302090899*h^3+47.190979547398*h^2-8236.9643808929*h+469370.79641163 3
 
3.7%
14.704488961779*h^3+6868.8239089906*h^2-1067950.5744023*h+55271700.314732 3
 
3.7%
Other values (36) 52
63.4%
2023-12-12T08:26:43.507709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 679
9.5%
2 612
 
8.5%
3 594
 
8.3%
4 549
 
7.7%
1 548
 
7.7%
6 521
 
7.3%
8 511
 
7.1%
9 509
 
7.1%
7 509
 
7.1%
5 473
 
6.6%
Other values (9) 1653
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5505
76.9%
Other Punctuation 656
 
9.2%
Lowercase Letter 358
 
5.0%
Math Symbol 231
 
3.2%
Modifier Symbol 202
 
2.8%
Dash Punctuation 194
 
2.7%
Open Punctuation 6
 
0.1%
Close Punctuation 6
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 679
12.3%
2 612
11.1%
3 594
10.8%
4 549
10.0%
1 548
10.0%
6 521
9.5%
8 511
9.3%
9 509
9.2%
7 509
9.2%
5 473
8.6%
Other Punctuation
ValueCountFrequency (%)
. 370
56.4%
* 286
43.6%
Lowercase Letter
ValueCountFrequency (%)
h 284
79.3%
e 74
 
20.7%
Math Symbol
ValueCountFrequency (%)
+ 231
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 202
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6800
95.0%
Latin 358
 
5.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 679
10.0%
2 612
9.0%
3 594
8.7%
4 549
8.1%
1 548
8.1%
6 521
7.7%
8 511
 
7.5%
9 509
 
7.5%
7 509
 
7.5%
5 473
 
7.0%
Other values (7) 1295
19.0%
Latin
ValueCountFrequency (%)
h 284
79.3%
e 74
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 679
9.5%
2 612
 
8.5%
3 594
 
8.3%
4 549
 
7.7%
1 548
 
7.7%
6 521
 
7.3%
8 511
 
7.1%
9 509
 
7.1%
7 509
 
7.1%
5 473
 
6.6%
Other values (9) 1653
23.1%

수위자료하한
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.20915
Minimum23
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T08:26:43.653007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile26
Q164.125
median150
Q3174.5
95-th percentile672
Maximum695
Range672
Interquartile range (IQR)110.375

Descriptive statistics

Standard deviation179.75091
Coefficient of variation (CV)1.0259219
Kurtosis3.7724362
Mean175.20915
Median Absolute Deviation (MAD)49.5
Skewness2.1465242
Sum14367.15
Variance32310.389
MonotonicityNot monotonic
2023-12-12T08:26:43.827740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
151.0 6
 
7.3%
695.0 4
 
4.9%
199.5 3
 
3.7%
150.0 3
 
3.7%
138.0 3
 
3.7%
30.0 3
 
3.7%
29.0 3
 
3.7%
166.0 3
 
3.7%
156.5 3
 
3.7%
151.4 3
 
3.7%
Other values (33) 48
58.5%
ValueCountFrequency (%)
23.0 2
2.4%
24.1 2
2.4%
26.0 2
2.4%
29.0 3
3.7%
30.0 3
3.7%
31.0 2
2.4%
43.5 1
 
1.2%
46.0 2
2.4%
48.65 1
 
1.2%
58.5 1
 
1.2%
ValueCountFrequency (%)
695.0 4
4.9%
672.0 2
2.4%
666.7 2
2.4%
360.0 1
 
1.2%
252.8 1
 
1.2%
234.5 1
 
1.2%
199.5 3
3.7%
197.2 2
2.4%
192.7 1
 
1.2%
190.5 1
 
1.2%

수위자료상한
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.0372
Minimum27
Maximum712.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T08:26:43.988211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile43
Q171.5
median157.75
Q3183.5
95-th percentile676.5
Maximum712.5
Range685.5
Interquartile range (IQR)112

Descriptive statistics

Standard deviation179.40064
Coefficient of variation (CV)0.95406998
Kurtosis3.8188409
Mean188.0372
Median Absolute Deviation (MAD)52.7
Skewness2.1603097
Sum15419.05
Variance32184.591
MonotonicityNot monotonic
2023-12-12T08:26:44.113974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
50.0 8
 
9.8%
155.5 6
 
7.3%
160.0 6
 
7.3%
676.5 4
 
4.9%
43.0 4
 
4.9%
712.5 4
 
4.9%
212.5 3
 
3.7%
179.0 3
 
3.7%
168.0 3
 
3.7%
166.0 3
 
3.7%
Other values (27) 38
46.3%
ValueCountFrequency (%)
27.0 2
 
2.4%
43.0 4
4.9%
50.0 8
9.8%
52.5 2
 
2.4%
53.65 1
 
1.2%
67.0 1
 
1.2%
67.6 1
 
1.2%
70.0 1
 
1.2%
71.5 2
 
2.4%
79.0 1
 
1.2%
ValueCountFrequency (%)
712.5 4
4.9%
676.5 4
4.9%
372.0 1
 
1.2%
269.0 1
 
1.2%
242.5 1
 
1.2%
212.5 3
3.7%
208.4 1
 
1.2%
208.0 1
 
1.2%
201.5 2
2.4%
200.2 1
 
1.2%

Interactions

2023-12-12T08:26:41.350373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:41.128754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:41.436109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:41.240793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:26:44.208178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예보유역명하도명적용시작일자적용종료일자여수로일련번호일련번호관계식수위자료하한수위자료상한
예보유역명1.0001.0000.9030.5820.0000.6551.0000.4280.443
하도명1.0001.0000.8650.0000.0000.4291.0001.0001.000
적용시작일자0.9030.8651.0000.8430.0000.8690.7050.2850.370
적용종료일자0.5820.0000.8431.0000.0000.0000.0000.1380.172
여수로일련번호0.0000.0000.0000.0001.0000.0001.0000.3780.388
일련번호0.6550.4290.8690.0000.0001.0001.0000.0000.000
관계식1.0001.0000.7050.0001.0001.0001.0001.0001.000
수위자료하한0.4281.0000.2850.1380.3780.0001.0001.0000.999
수위자료상한0.4431.0000.3700.1720.3880.0001.0000.9991.000
2023-12-12T08:26:44.367209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하도명일련번호여수로일련번호적용종료일자예보유역명
하도명1.0000.2500.0000.0000.794
일련번호0.2501.0000.0000.0000.632
여수로일련번호0.0000.0001.0000.0000.000
적용종료일자0.0000.0000.0001.0000.400
예보유역명0.7940.6320.0000.4001.000
2023-12-12T08:26:44.485083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위자료하한수위자료상한예보유역명하도명적용종료일자여수로일련번호일련번호
수위자료하한1.0000.9890.2210.7780.0840.1640.000
수위자료상한0.9891.0000.2300.7780.1060.1690.000
예보유역명0.2210.2301.0000.7940.4000.0000.632
하도명0.7780.7780.7941.0000.0000.0000.250
적용종료일자0.0840.1060.4000.0001.0000.0000.000
여수로일련번호0.1640.1690.0000.0000.0001.0000.000
일련번호0.0000.0000.6320.2500.0000.0001.000

Missing values

2023-12-12T08:26:41.552425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:26:41.669023image/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

예보유역명하도명적용시작일자적용종료일자여수로일련번호일련번호관계식수위자료하한수위자료상한
0한강1001010광동댐2000-01-012017-05-1011-0.12774970873267*h^3+260.42408281241*h^2-176884.75491648*h+40030894.607591666.7676.5
1한강1001010광동댐2000-01-012017-05-1021-0.49179445789605*h^3+1002.1657809501*h^2-680642.82137166*h+154072077.27935672.0676.5
2한강1001010광동댐2017-05-119999-12-3111-0.12774970873267*h^3+260.42408281241*h^2-176884.75491648*h+40030894.607591666.7676.5
3한강1001010광동댐2017-05-119999-12-3121-0.49179445789605*h^3+1002.1657809501*h^2-680642.82137166*h+154072077.27935672.0676.5
4한강10010502000-01-012017-05-1011-0.10966072266373*h^3+235.00898736051*h^2-167733.61477186*h+39872994.399615695.0712.5
5한강10010502000-01-012017-05-10216.5895531550169e-02*h^3-1.3711014887208e+02*h^2+9.5121628445769e+04*h-2.2003181609320e+07695.0712.5
6한강10010502017-05-119999-12-3111-0.10966072266373*h^3+235.00898736051*h^2-167733.61477186*h+39872994.399615695.0712.5
7한강10010502017-05-119999-12-31216.5895531550169e-02*h^3-1.3711014887208e+02*h^2+9.5121628445769e+04*h-2.2003181609320e+07695.0712.5
8한강1003180충주댐2000-01-012017-05-1011-0.078876051484713*h^3+37.492035138761*h^2-5666.1103162762*h+276491.20934927126.0148.0
9한강1003180충주댐2017-05-119999-12-3111-0.078876051484713*h^3+37.492035138761*h^2-5666.1103162762*h+276491.20934927126.0148.0
예보유역명하도명적용시작일자적용종료일자여수로일련번호일련번호관계식수위자료하한수위자료상한
72태화강2201050대곡댐2000-01-019999-12-3111-0.94748393458622*h^3+348.42440498921*h^2-42636.816206202*h+1736437.6307222117.5125.2
73태화강2201060사연댐2000-01-019999-12-3111-2.9840471993773*h^3+593.29005963234*h^2-38889.356466885*h+842067.5498691460.067.0
74금강3001160용담댐2000-01-019999-12-3111-0.092701421664941*h^3+77.749128508719*h^2-21511.699327383*h+1967048.9182917252.8269.0
75금강3008070대청댐2000-01-019999-12-3111-0.065140540576977*h^3+18.626242934378*h^2-1563.012352365*h+40796.60256977464.582.0
76금강서해3203640보령댐2000-01-019999-12-3111-0.041325646317901*h^3+11.920321023145*h^2-1001.7657416542*h+26118.46179787664.079.0
77섬진강4002010섬진강댐2000-01-019999-12-3111-0.36995538340693*h^3+222.5293228576*h^2-44540.384609221*h+2966932.6850235192.7197.7
78섬진강4007110주암댐2000-01-019999-12-3111-0.0037289919643939*h^5+1.9765443077765*h^4-418.89085210358*h^3+44374.076916063*h^2-2349727.2130241*h+49758334.70035998.5115.0
79영산강50010152013-01-019999-12-31111.0702280478023e+00*(12-0.2*(h-43.50))*(h-43.50)^1.543.553.65
80영산강50030152013-01-019999-12-31111.2056989835946e+00*(10-0.2*(h-58.50))*(h-58.50)^1.558.567.6
81탐진강5101020장흥댐2000-01-019999-12-3111-0.074288701494567*h^3+20.51257639419*h^2-1776.3847911889*h+49306.08891108371.084.0