Overview

Dataset statistics

Number of variables20
Number of observations61
Missing cells190
Missing cells (%)15.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory177.2 B

Variable types

Text2
Categorical1
Numeric15
DateTime2

Dataset

Descriptionㅁ 개 요 : K-water에서 관리중인 댐(다목적댐, 용수댐) 및 보의 제원정보 입니다. 2022년 12월까지 변동사항 없습니다. ㅁ 제공항목 * 형식 * 높이[m] * 길이[m] * 정상표고 [EL.m] * 체적[㎥] * 유역면적[㎢] * 저수면적[㎡] * 계획홍수위[EL.m] * 상시만수위[EL.m] * 홍수기제한수위[EL.m] * 월류정표고[EL.m] * 저수위[EL.m] * 총저수용량[㎥] * 유효저수용량[㎥] * 홍수조절용량[㎥] ㅁ 보유기간 : 매년 업데이트 ㅁ 제공형식 :파일(XLS, CSV), 오픈API(XML, JSON)
URLhttps://www.data.go.kr/data/15083336/fileData.do

Alerts

높이(미터) is highly overall correlated with 정상표고(EL_미터) and 10 other fieldsHigh correlation
길이(미터) is highly overall correlated with 체적(천미터세제곱) and 3 other fieldsHigh correlation
정상표고(EL_미터) is highly overall correlated with 높이(미터) and 5 other fieldsHigh correlation
체적(천미터세제곱) is highly overall correlated with 높이(미터) and 6 other fieldsHigh correlation
유역면적(킬로미터제곱) is highly overall correlated with 높이(미터) and 6 other fieldsHigh correlation
연간용수공급량(백만미터세제곱) is highly overall correlated with 높이(미터) and 8 other fieldsHigh correlation
저수면적(킬로미터제곱) is highly overall correlated with 높이(미터) and 7 other fieldsHigh correlation
계획홍수위(EL_미터) is highly overall correlated with 높이(미터) and 5 other fieldsHigh correlation
상시만수위(EL_미터) is highly overall correlated with 정상표고(EL_미터) and 5 other fieldsHigh correlation
홍수기제한수위(EL_미터) is highly overall correlated with 정상표고(EL_미터) and 6 other fieldsHigh correlation
월류정표고(EL_미터) is highly overall correlated with 높이(미터) and 5 other fieldsHigh correlation
저수위(EL_미터) is highly overall correlated with 높이(미터) and 5 other fieldsHigh correlation
총저수용량(백만미터세제곱) is highly overall correlated with 높이(미터) and 6 other fieldsHigh correlation
유효저수용량(백만미터세제곱) is highly overall correlated with 높이(미터) and 6 other fieldsHigh correlation
홍수조절용량(백만미터세제곱) is highly overall correlated with 높이(미터) and 6 other fieldsHigh correlation
높이(미터) has 16 (26.2%) missing valuesMissing
정상표고(EL_미터) has 16 (26.2%) missing valuesMissing
체적(천미터세제곱) has 16 (26.2%) missing valuesMissing
연간용수공급량(백만미터세제곱) has 16 (26.2%) missing valuesMissing
저수면적(킬로미터제곱) has 17 (27.9%) missing valuesMissing
상시만수위(EL_미터) has 17 (27.9%) missing valuesMissing
홍수기제한수위(EL_미터) has 26 (42.6%) missing valuesMissing
월류정표고(EL_미터) has 17 (27.9%) missing valuesMissing
저수위(EL_미터) has 17 (27.9%) missing valuesMissing
유효저수용량(백만미터세제곱) has 16 (26.2%) missing valuesMissing
홍수조절용량(백만미터세제곱) has 16 (26.2%) missing valuesMissing
댐이름 has unique valuesUnique
높이(미터) has 5 (8.2%) zerosZeros
체적(천미터세제곱) has 6 (9.8%) zerosZeros
유역면적(킬로미터제곱) has 6 (9.8%) zerosZeros
연간용수공급량(백만미터세제곱) has 11 (18.0%) zerosZeros
저수면적(킬로미터제곱) has 8 (13.1%) zerosZeros
상시만수위(EL_미터) has 2 (3.3%) zerosZeros
홍수기제한수위(EL_미터) has 2 (3.3%) zerosZeros
유효저수용량(백만미터세제곱) has 2 (3.3%) zerosZeros
홍수조절용량(백만미터세제곱) has 22 (36.1%) zerosZeros

Reproduction

Analysis started2023-12-12 07:13:44.538461
Analysis finished2023-12-12 07:14:11.084075
Duration26.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Text

UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-12T16:14:11.291620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length3.0491803
Min length2

Characters and Unicode

Total characters186
Distinct characters87
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)100.0%

Sample

1st row낙동강하굿둑
2nd row주암(조)
3rd row횡성
4th row주암(본)
5th row부안
ValueCountFrequency (%)
낙동강하굿둑 1
 
1.6%
대암 1
 
1.6%
연초 1
 
1.6%
구천 1
 
1.6%
수어 1
 
1.6%
평림 1
 
1.6%
감포 1
 
1.6%
대곡 1
 
1.6%
충주조정지 1
 
1.6%
안동조정지 1
 
1.6%
Other values (51) 51
83.6%
2023-12-12T16:14:11.731647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
9.7%
9
 
4.8%
7
 
3.8%
7
 
3.8%
7
 
3.8%
7
 
3.8%
6
 
3.2%
5
 
2.7%
5
 
2.7%
4
 
2.2%
Other values (77) 111
59.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 179
96.2%
Open Punctuation 3
 
1.6%
Close Punctuation 3
 
1.6%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18
 
10.1%
9
 
5.0%
7
 
3.9%
7
 
3.9%
7
 
3.9%
7
 
3.9%
6
 
3.4%
5
 
2.8%
5
 
2.8%
4
 
2.2%
Other values (74) 104
58.1%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 179
96.2%
Common 7
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18
 
10.1%
9
 
5.0%
7
 
3.9%
7
 
3.9%
7
 
3.9%
7
 
3.9%
6
 
3.4%
5
 
2.8%
5
 
2.8%
4
 
2.2%
Other values (74) 104
58.1%
Common
ValueCountFrequency (%)
( 3
42.9%
) 3
42.9%
3 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 179
96.2%
ASCII 7
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
18
 
10.1%
9
 
5.0%
7
 
3.9%
7
 
3.9%
7
 
3.9%
7
 
3.9%
6
 
3.4%
5
 
2.8%
5
 
2.8%
4
 
2.2%
Other values (74) 104
58.1%
ASCII
ValueCountFrequency (%)
( 3
42.9%
) 3
42.9%
3 1
 
14.3%

하천
Text

Distinct34
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-12T16:14:11.941945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8032787
Min length2

Characters and Unicode

Total characters171
Distinct characters54
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)41.0%

Sample

1st row낙동강
2nd row이사천
3rd row섬강
4th row보성강
5th row직소천
ValueCountFrequency (%)
낙동강 15
24.6%
금강 6
 
9.8%
한강 3
 
4.9%
남한강 2
 
3.3%
대곡천 2
 
3.3%
이사천 2
 
3.3%
황강 2
 
3.3%
영산강 2
 
3.3%
북한강 2
 
3.3%
수어천 1
 
1.6%
Other values (24) 24
39.3%
2023-12-12T16:14:12.305025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
22.8%
25
14.6%
16
 
9.4%
15
 
8.8%
8
 
4.7%
6
 
3.5%
3
 
1.8%
3
 
1.8%
2
 
1.2%
2
 
1.2%
Other values (44) 52
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
99.4%
Decimal Number 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
39
22.9%
25
14.7%
16
 
9.4%
15
 
8.8%
8
 
4.7%
6
 
3.5%
3
 
1.8%
3
 
1.8%
2
 
1.2%
2
 
1.2%
Other values (43) 51
30.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
99.4%
Common 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
39
22.9%
25
14.7%
16
 
9.4%
15
 
8.8%
8
 
4.7%
6
 
3.5%
3
 
1.8%
3
 
1.8%
2
 
1.2%
2
 
1.2%
Other values (43) 51
30.0%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
99.4%
ASCII 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
39
22.9%
25
14.7%
16
 
9.4%
15
 
8.8%
8
 
4.7%
6
 
3.5%
3
 
1.8%
3
 
1.8%
2
 
1.2%
2
 
1.2%
Other values (43) 51
30.0%
ASCII
ValueCountFrequency (%)
2 1
100.0%

형식
Categorical

Distinct6
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size620.0 B
E.C.R.D
18 
C.G.W
16 
C.G.D
10 
C.F.R.D
C.G_E.C.R.D

Length

Max length11
Median length7
Mean length6.4098361
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC.G_E.C.R.D
2nd rowE.C.R.D
3rd rowE.C.R.D
4th rowE.C.R.D
5th rowC.F.R.D

Common Values

ValueCountFrequency (%)
E.C.R.D 18
29.5%
C.G.W 16
26.2%
C.G.D 10
16.4%
C.F.R.D 9
14.8%
C.G_E.C.R.D 6
 
9.8%
E.F 2
 
3.3%

Length

2023-12-12T16:14:12.475197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:14:12.616767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e.c.r.d 18
29.5%
c.g.w 16
26.2%
c.g.d 10
16.4%
c.f.r.d 9
14.8%
c.g_e.c.r.d 6
 
9.8%
e.f 2
 
3.3%

높이(미터)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct37
Distinct (%)82.2%
Missing16
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean51.155556
Minimum0
Maximum125
Zeros5
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:12.740876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132.5
median50
Q367
95-th percentile99.42
Maximum125
Range125
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation30.839176
Coefficient of variation (CV)0.60285097
Kurtosis0.065998677
Mean51.155556
Median Absolute Deviation (MAD)17.5
Skewness0.33694271
Sum2302
Variance951.0548
MonotonicityNot monotonic
2023-12-12T16:14:12.905078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 5
 
8.2%
50.0 3
 
4.9%
64.0 2
 
3.3%
58.5 2
 
3.3%
26.0 1
 
1.6%
39.5 1
 
1.6%
22.6 1
 
1.6%
48.5 1
 
1.6%
52.0 1
 
1.6%
35.0 1
 
1.6%
Other values (27) 27
44.3%
(Missing) 16
26.2%
ValueCountFrequency (%)
0.0 5
8.2%
18.7 1
 
1.6%
22.0 1
 
1.6%
22.6 1
 
1.6%
24.5 1
 
1.6%
26.0 1
 
1.6%
27.0 1
 
1.6%
32.5 1
 
1.6%
34.0 1
 
1.6%
35.0 1
 
1.6%
ValueCountFrequency (%)
125.0 1
1.6%
123.0 1
1.6%
99.9 1
1.6%
97.5 1
1.6%
96.0 1
1.6%
89.5 1
1.6%
83.5 1
1.6%
83.0 1
1.6%
73.0 1
1.6%
72.0 1
1.6%

길이(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.99508
Minimum108
Maximum2230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:13.049583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile192
Q1286
median374
Q3512
95-th percentile690
Maximum2230
Range2122
Interquartile range (IQR)226

Descriptive statistics

Standard deviation294.90874
Coefficient of variation (CV)0.68584211
Kurtosis23.299867
Mean429.99508
Median Absolute Deviation (MAD)100
Skewness4.1475773
Sum26229.7
Variance86971.162
MonotonicityNot monotonic
2023-12-12T16:14:13.209676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234.0 2
 
3.3%
300.0 2
 
3.3%
400.0 2
 
3.3%
472.0 2
 
3.3%
286.0 1
 
1.6%
437.0 1
 
1.6%
390.5 1
 
1.6%
108.0 1
 
1.6%
192.0 1
 
1.6%
480.7 1
 
1.6%
Other values (47) 47
77.0%
ValueCountFrequency (%)
108.0 1
1.6%
120.0 1
1.6%
184.0 1
1.6%
192.0 1
1.6%
194.7 1
1.6%
205.0 1
1.6%
218.0 1
1.6%
223.5 1
1.6%
234.0 2
3.3%
250.0 1
1.6%
ValueCountFrequency (%)
2230.0 1
1.6%
1126.0 1
1.6%
954.0 1
1.6%
690.0 1
1.6%
658.0 1
1.6%
612.0 1
1.6%
601.0 1
1.6%
580.0 1
1.6%
562.6 1
1.6%
549.0 1
1.6%

정상표고(EL_미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)95.6%
Missing16
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean137.00222
Minimum9.2
Maximum678.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:13.389267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile32.94
Q166
median115
Q3181
95-th percentile269.7
Maximum678.5
Range669.3
Interquartile range (IQR)115

Descriptive statistics

Standard deviation112.98669
Coefficient of variation (CV)0.82470696
Kurtosis11.310771
Mean137.00222
Median Absolute Deviation (MAD)60
Skewness2.7453478
Sum6165.1
Variance12765.991
MonotonicityNot monotonic
2023-12-12T16:14:13.551885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
168.0 2
 
3.3%
115.0 2
 
3.3%
36.7 1
 
1.6%
66.0 1
 
1.6%
117.0 1
 
1.6%
155.1 1
 
1.6%
162.0 1
 
1.6%
66.4 1
 
1.6%
32.0 1
 
1.6%
55.0 1
 
1.6%
Other values (33) 33
54.1%
(Missing) 16
26.2%
ValueCountFrequency (%)
9.2 1
1.6%
28.0 1
1.6%
32.0 1
1.6%
36.7 1
1.6%
44.0 1
1.6%
45.0 1
1.6%
46.9 1
1.6%
49.0 1
1.6%
51.0 1
1.6%
52.0 1
1.6%
ValueCountFrequency (%)
678.5 1
1.6%
368.5 1
1.6%
270.0 1
1.6%
268.5 1
1.6%
241.0 1
1.6%
213.0 1
1.6%
208.4 1
1.6%
203.0 1
1.6%
201.4 1
1.6%
200.0 1
1.6%

체적(천미터세제곱)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)86.7%
Missing16
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean1416.7778
Minimum0
Maximum9591
Zeros6
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:13.727684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1254
median877
Q31460
95-th percentile4775
Maximum9591
Range9591
Interquartile range (IQR)1206

Descriptive statistics

Standard deviation1966.855
Coefficient of variation (CV)1.3882594
Kurtosis8.1722509
Mean1416.7778
Median Absolute Deviation (MAD)623
Skewness2.7264714
Sum63755
Variance3868518.8
MonotonicityNot monotonic
2023-12-12T16:14:13.897299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 6
 
9.8%
2206 2
 
3.3%
1573 1
 
1.6%
757 1
 
1.6%
616 1
 
1.6%
1009 1
 
1.6%
1319 1
 
1.6%
960 1
 
1.6%
696 1
 
1.6%
290 1
 
1.6%
Other values (29) 29
47.5%
(Missing) 16
26.2%
ValueCountFrequency (%)
0 6
9.8%
108 1
 
1.6%
177 1
 
1.6%
190 1
 
1.6%
203 1
 
1.6%
227 1
 
1.6%
254 1
 
1.6%
290 1
 
1.6%
410 1
 
1.6%
528 1
 
1.6%
ValueCountFrequency (%)
9591 1
1.6%
7905 1
1.6%
4965 1
1.6%
4015 1
1.6%
3943 1
1.6%
3423 1
1.6%
2206 2
3.3%
2189 1
1.6%
1573 1
1.6%
1506 1
1.6%

유역면적(킬로미터제곱)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3525.5902
Minimum0
Maximum23560
Zeros6
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:14.063822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q149
median500
Q36648
95-th percentile14248
Maximum23560
Range23560
Interquartile range (IQR)6599

Descriptive statistics

Standard deviation5487.3817
Coefficient of variation (CV)1.5564434
Kurtosis3.0569814
Mean3525.5902
Median Absolute Deviation (MAD)500
Skewness1.8307778
Sum215061
Variance30111358
MonotonicityNot monotonic
2023-12-12T16:14:14.262429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
9.8%
23560 1
 
1.6%
67 1
 
1.6%
77 1
 
1.6%
7 1
 
1.6%
12 1
 
1.6%
13 1
 
1.6%
49 1
 
1.6%
20 1
 
1.6%
4 1
 
1.6%
Other values (46) 46
75.4%
ValueCountFrequency (%)
0 6
9.8%
1 1
 
1.6%
4 1
 
1.6%
7 1
 
1.6%
12 1
 
1.6%
13 1
 
1.6%
20 1
 
1.6%
29 1
 
1.6%
33 1
 
1.6%
41 1
 
1.6%
ValueCountFrequency (%)
23560 1
1.6%
20697 1
1.6%
15074 1
1.6%
14248 1
1.6%
11803 1
1.6%
11667 1
1.6%
11115 1
1.6%
11040 1
1.6%
10972 1
1.6%
9557 1
1.6%

연간용수공급량(백만미터세제곱)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)73.3%
Missing16
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean277.42222
Minimum0
Maximum3380
Zeros11
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:14.453641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median34
Q3219
95-th percentile1155.6
Maximum3380
Range3380
Interquartile range (IQR)217

Descriptive statistics

Standard deviation593.09349
Coefficient of variation (CV)2.137873
Kurtosis17.28396
Mean277.42222
Median Absolute Deviation (MAD)34
Skewness3.7934035
Sum12484
Variance351759.89
MonotonicityNot monotonic
2023-12-12T16:14:14.628745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 11
18.0%
15 2
 
3.3%
107 2
 
3.3%
35 1
 
1.6%
270 1
 
1.6%
32 1
 
1.6%
2 1
 
1.6%
12 1
 
1.6%
30 1
 
1.6%
8 1
 
1.6%
Other values (23) 23
37.7%
(Missing) 16
26.2%
ValueCountFrequency (%)
0 11
18.0%
2 1
 
1.6%
6 1
 
1.6%
8 1
 
1.6%
12 1
 
1.6%
15 2
 
3.3%
18 1
 
1.6%
21 1
 
1.6%
26 1
 
1.6%
30 1
 
1.6%
ValueCountFrequency (%)
3380 1
1.6%
1649 1
1.6%
1213 1
1.6%
926 1
1.6%
750 1
1.6%
650 1
1.6%
599 1
1.6%
592 1
1.6%
573 1
1.6%
435 1
1.6%

저수면적(킬로미터제곱)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)70.5%
Missing17
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean16.393182
Minimum0
Maximum180.7
Zeros8
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:14.830658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median2.3
Q313.975
95-th percentile72.38
Maximum180.7
Range180.7
Interquartile range (IQR)13.475

Descriptive statistics

Standard deviation33.658873
Coefficient of variation (CV)2.0532239
Kurtosis13.295315
Mean16.393182
Median Absolute Deviation (MAD)2.3
Skewness3.3543332
Sum721.3
Variance1132.9197
MonotonicityNot monotonic
2023-12-12T16:14:14.973705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 8
 
13.1%
1.5 4
 
6.6%
0.5 2
 
3.3%
7.8 2
 
3.3%
3.0 2
 
3.3%
70.0 1
 
1.6%
2.4 1
 
1.6%
0.2 1
 
1.6%
0.9 1
 
1.6%
0.6 1
 
1.6%
Other values (21) 21
34.4%
(Missing) 17
27.9%
ValueCountFrequency (%)
0.0 8
13.1%
0.2 1
 
1.6%
0.3 1
 
1.6%
0.5 2
 
3.3%
0.6 1
 
1.6%
0.9 1
 
1.6%
1.0 1
 
1.6%
1.4 1
 
1.6%
1.5 4
6.6%
1.9 1
 
1.6%
ValueCountFrequency (%)
180.7 1
1.6%
97.0 1
1.6%
72.8 1
1.6%
70.0 1
1.6%
51.5 1
1.6%
36.2 1
1.6%
33.0 1
1.6%
28.2 1
1.6%
26.5 1
1.6%
26.4 1
1.6%

계획홍수위(EL_미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.73443
Minimum3.7
Maximum675.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:15.147517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile13.1
Q136.2
median67.3
Q3159.3
95-th percentile264.6
Maximum675.3
Range671.6
Interquartile range (IQR)123.1

Descriptive statistics

Standard deviation107.90898
Coefficient of variation (CV)1.0205662
Kurtosis12.185023
Mean105.73443
Median Absolute Deviation (MAD)43.8
Skewness2.8257862
Sum6449.8
Variance11644.348
MonotonicityNot monotonic
2023-12-12T16:14:15.352705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.6 2
 
3.3%
3.7 1
 
1.6%
52.9 1
 
1.6%
94.5 1
 
1.6%
66.2 1
 
1.6%
111.3 1
 
1.6%
41.2 1
 
1.6%
122.7 1
 
1.6%
67.3 1
 
1.6%
98.8 1
 
1.6%
Other values (50) 50
82.0%
ValueCountFrequency (%)
3.7 1
1.6%
7.1 1
1.6%
11.6 1
1.6%
13.1 1
1.6%
13.7 1
1.6%
17.6 1
1.6%
18.6 1
1.6%
21.9 1
1.6%
23.5 1
1.6%
24.0 1
1.6%
ValueCountFrequency (%)
675.3 1
1.6%
364.9 1
1.6%
265.5 1
1.6%
264.6 1
1.6%
238.5 1
1.6%
210.2 1
1.6%
205.1 1
1.6%
198.6 1
1.6%
198.0 1
1.6%
197.7 1
1.6%

상시만수위(EL_미터)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct41
Distinct (%)93.2%
Missing17
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean125.64773
Minimum0
Maximum672
Zeros2
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:15.553675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.475
Q148.375
median105.8
Q3166.25
95-th percentile259.375
Maximum672
Range672
Interquartile range (IQR)117.875

Descriptive statistics

Standard deviation114.24151
Coefficient of variation (CV)0.90922063
Kurtosis11.571284
Mean125.64773
Median Absolute Deviation (MAD)57.55
Skewness2.7887935
Sum5528.5
Variance13051.122
MonotonicityNot monotonic
2023-12-12T16:14:15.704536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 2
 
3.3%
30.0 2
 
3.3%
108.5 2
 
3.3%
204.0 1
 
1.6%
64.0 1
 
1.6%
156.8 1
 
1.6%
60.0 1
 
1.6%
41.2 1
 
1.6%
48.5 1
 
1.6%
43.9 1
 
1.6%
Other values (31) 31
50.8%
(Missing) 17
27.9%
ValueCountFrequency (%)
0.0 2
3.3%
23.5 1
1.6%
30.0 2
3.3%
31.0 1
1.6%
40.0 1
1.6%
41.0 1
1.6%
41.2 1
1.6%
43.9 1
1.6%
48.0 1
1.6%
48.5 1
1.6%
ValueCountFrequency (%)
672.0 1
1.6%
364.0 1
1.6%
263.5 1
1.6%
236.0 1
1.6%
207.2 1
1.6%
204.0 1
1.6%
196.5 1
1.6%
195.0 1
1.6%
193.5 1
1.6%
180.0 1
1.6%

홍수기제한수위(EL_미터)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)94.3%
Missing26
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean121.56286
Minimum0
Maximum672
Zeros2
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:16.149020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.1
Q145.95
median98
Q3156.75
95-th percentile291.65
Maximum672
Range672
Interquartile range (IQR)110.8

Descriptive statistics

Standard deviation124.33502
Coefficient of variation (CV)1.0228044
Kurtosis10.924653
Mean121.56286
Median Absolute Deviation (MAD)56.8
Skewness2.8503247
Sum4254.7
Variance15459.198
MonotonicityNot monotonic
2023-12-12T16:14:16.316615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 2
 
3.3%
30.0 2
 
3.3%
57.5 1
 
1.6%
103.1 1
 
1.6%
98.0 1
 
1.6%
23.5 1
 
1.6%
65.1 1
 
1.6%
60.0 1
 
1.6%
40.0 1
 
1.6%
109.7 1
 
1.6%
Other values (23) 23
37.7%
(Missing) 26
42.6%
ValueCountFrequency (%)
0.0 2
3.3%
23.0 1
1.6%
23.5 1
1.6%
30.0 2
3.3%
40.0 1
1.6%
41.2 1
1.6%
43.9 1
1.6%
48.0 1
1.6%
48.5 1
1.6%
57.5 1
1.6%
ValueCountFrequency (%)
672.0 1
1.6%
362.0 1
1.6%
261.5 1
1.6%
236.0 1
1.6%
193.5 1
1.6%
190.3 1
1.6%
178.2 1
1.6%
161.7 1
1.6%
156.8 1
1.6%
156.7 1
1.6%

월류정표고(EL_미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)97.7%
Missing17
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean125.83182
Minimum18.5
Maximum672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:16.449582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.5
5-th percentile29.15
Q154.25
median103.5
Q3161.5
95-th percentile250.055
Maximum672
Range653.5
Interquartile range (IQR)107.25

Descriptive statistics

Standard deviation111.33007
Coefficient of variation (CV)0.88475297
Kurtosis13.031268
Mean125.83182
Median Absolute Deviation (MAD)54.15
Skewness3.0176009
Sum5536.6
Variance12394.385
MonotonicityNot monotonic
2023-12-12T16:14:16.567256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
64.0 2
 
3.3%
360.0 1
 
1.6%
112.0 1
 
1.6%
150.0 1
 
1.6%
156.8 1
 
1.6%
60.0 1
 
1.6%
30.0 1
 
1.6%
48.5 1
 
1.6%
43.9 1
 
1.6%
48.0 1
 
1.6%
Other values (33) 33
54.1%
(Missing) 17
27.9%
ValueCountFrequency (%)
18.5 1
1.6%
24.4 1
1.6%
29.0 1
1.6%
30.0 1
1.6%
31.0 1
1.6%
40.0 1
1.6%
41.2 1
1.6%
43.9 1
1.6%
48.0 1
1.6%
48.5 1
1.6%
ValueCountFrequency (%)
672.0 1
1.6%
360.0 1
1.6%
252.8 1
1.6%
234.5 1
1.6%
199.5 1
1.6%
197.2 1
1.6%
192.7 1
1.6%
190.5 1
1.6%
185.5 1
1.6%
167.0 1
1.6%

저수위(EL_미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)88.6%
Missing17
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean109.72045
Minimum18.5
Maximum662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:16.700160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.5
5-th percentile22.575
Q144.875
median88
Q3142.5
95-th percentile225.425
Maximum662
Range643.5
Interquartile range (IQR)97.625

Descriptive statistics

Standard deviation108.06891
Coefficient of variation (CV)0.98494772
Kurtosis15.687303
Mean109.72045
Median Absolute Deviation (MAD)49.5
Skewness3.3939924
Sum4827.7
Variance11678.89
MonotonicityNot monotonic
2023-12-12T16:14:16.842248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
150.0 2
 
3.3%
160.0 2
 
3.3%
55.0 2
 
3.3%
60.0 2
 
3.3%
23.0 2
 
3.3%
45.0 1
 
1.6%
44.5 1
 
1.6%
31.0 1
 
1.6%
36.3 1
 
1.6%
58.0 1
 
1.6%
Other values (29) 29
47.5%
(Missing) 17
27.9%
ValueCountFrequency (%)
18.5 1
1.6%
21.0 1
1.6%
22.5 1
1.6%
23.0 2
3.3%
25.0 1
1.6%
31.0 1
1.6%
32.0 1
1.6%
36.3 1
1.6%
44.0 1
1.6%
44.5 1
1.6%
ValueCountFrequency (%)
662.0 1
1.6%
333.0 1
1.6%
228.5 1
1.6%
208.0 1
1.6%
181.0 1
1.6%
165.0 1
1.6%
160.0 2
3.3%
154.5 1
1.6%
150.0 2
3.3%
140.0 1
1.6%
Distinct39
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size620.0 B
Minimum1961-08-01 00:00:00
Maximum2012-06-01 00:00:00
2023-12-12T16:14:16.988090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:17.159621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
Distinct36
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size620.0 B
Minimum1964-12-01 00:00:00
Maximum2018-12-01 00:00:00
2023-12-12T16:14:17.303936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:17.471367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

총저수용량(백만미터세제곱)
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.81967
Minimum1
Maximum2900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:17.694870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median49
Q3181
95-th percentile1490
Maximum2900
Range2899
Interquartile range (IQR)170

Descriptive statistics

Standard deviation637.13441
Coefficient of variation (CV)2.2527938
Kurtosis9.9955632
Mean282.81967
Median Absolute Deviation (MAD)41
Skewness3.2072461
Sum17252
Variance405940.25
MonotonicityNot monotonic
2023-12-12T16:14:17.859664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
4.9%
9 3
 
4.9%
11 2
 
3.3%
28 2
 
3.3%
10 2
 
3.3%
5 2
 
3.3%
297 1
 
1.6%
96 1
 
1.6%
18 1
 
1.6%
29 1
 
1.6%
Other values (43) 43
70.5%
ValueCountFrequency (%)
1 1
 
1.6%
2 3
4.9%
3 1
 
1.6%
4 1
 
1.6%
5 2
3.3%
6 1
 
1.6%
8 1
 
1.6%
9 3
4.9%
10 2
3.3%
11 2
3.3%
ValueCountFrequency (%)
2900 1
1.6%
2750 1
1.6%
2630 1
1.6%
1490 1
1.6%
1248 1
1.6%
815 1
1.6%
790 1
1.6%
595 1
1.6%
466 1
1.6%
457 1
1.6%

유효저수용량(백만미터세제곱)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct38
Distinct (%)84.4%
Missing16
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean218.17778
Minimum0
Maximum1900
Zeros2
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:18.042864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median36
Q3210
95-th percentile958
Maximum1900
Range1900
Interquartile range (IQR)202

Descriptive statistics

Standard deviation421.52123
Coefficient of variation (CV)1.9320081
Kurtosis8.8934776
Mean218.17778
Median Absolute Deviation (MAD)35
Skewness2.9257662
Sum9818
Variance177680.15
MonotonicityNot monotonic
2023-12-12T16:14:18.192200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
8 3
 
4.9%
2 3
 
4.9%
1 2
 
3.3%
5 2
 
3.3%
0 2
 
3.3%
40 1
 
1.6%
25 1
 
1.6%
4 1
 
1.6%
73 1
 
1.6%
3 1
 
1.6%
Other values (28) 28
45.9%
(Missing) 16
26.2%
ValueCountFrequency (%)
0 2
3.3%
1 2
3.3%
2 3
4.9%
3 1
 
1.6%
4 1
 
1.6%
5 2
3.3%
8 3
4.9%
9 1
 
1.6%
13 1
 
1.6%
17 1
 
1.6%
ValueCountFrequency (%)
1900 1
1.6%
1789 1
1.6%
1000 1
1.6%
790 1
1.6%
672 1
1.6%
560 1
1.6%
424 1
1.6%
370 1
1.6%
352 1
1.6%
300 1
1.6%

홍수조절용량(백만미터세제곱)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)44.4%
Missing16
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean58.6
Minimum0
Maximum616
Zeros22
Zeros (%)36.1%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T16:14:18.353667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q360
95-th percentile270
Maximum616
Range616
Interquartile range (IQR)60

Descriptive statistics

Standard deviation129.9609
Coefficient of variation (CV)2.2177629
Kurtosis9.5865129
Mean58.6
Median Absolute Deviation (MAD)3
Skewness3.0339972
Sum2637
Variance16889.836
MonotonicityNot monotonic
2023-12-12T16:14:18.490245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 22
36.1%
10 2
 
3.3%
4 2
 
3.3%
270 2
 
3.3%
80 2
 
3.3%
71 1
 
1.6%
3 1
 
1.6%
500 1
 
1.6%
616 1
 
1.6%
110 1
 
1.6%
Other values (10) 10
16.4%
(Missing) 16
26.2%
ValueCountFrequency (%)
0 22
36.1%
3 1
 
1.6%
4 2
 
3.3%
6 1
 
1.6%
8 1
 
1.6%
9 1
 
1.6%
10 2
 
3.3%
12 1
 
1.6%
20 1
 
1.6%
32 1
 
1.6%
ValueCountFrequency (%)
616 1
1.6%
500 1
1.6%
270 2
3.3%
250 1
1.6%
137 1
1.6%
110 1
1.6%
80 2
3.3%
75 1
1.6%
71 1
1.6%
60 1
1.6%

Interactions

2023-12-12T16:14:08.380076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:45.401062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.052150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.411304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:49.923826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:51.589658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.053865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.750608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.445473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.287904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.885567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.613790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.556124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.005585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.695865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.488365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:45.492978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.139402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.529768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.009144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:51.684626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.151967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.864212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.591202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.385010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.984540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.721250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.625282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.119352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.814701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.575938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:45.578734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.212314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.605298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.073858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:51.798128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.231086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.967969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.689281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.466548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.098609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.816411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.704985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.205586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.915758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.707741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:45.667997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.293153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.703418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.163886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:51.909004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.325048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.087239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.826329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.574749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.200895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.926820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.785203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.328639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.036376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.809962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:45.793857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.371232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.804669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.252702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.009995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.433385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.182582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.941342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.672800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.323154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.068619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.862000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.426899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.150735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.898769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:45.889684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.445082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.890258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.331135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.107931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.523362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.299306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:57.271450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.773359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.443137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.207870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.949471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.529596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.260267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.996193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:46.031884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.538677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.982934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.434342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.204223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.754070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.429394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:57.372754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.878353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.555919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.326580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.054187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.640092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.382915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:09.100588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:46.161023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.629510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:49.094278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.538772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.289095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.870708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.540992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:57.466097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.970387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.665446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.439161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.152805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.748188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.504173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:09.200659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:46.277598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.721909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:49.214730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.911453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.386124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:53.985150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.636104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:57.554602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.091180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.785739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.546765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.247681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.861330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.618885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:09.296917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:46.384935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:47.821238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:49.324789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:50.997561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.481101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.093655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.737756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:57.641047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.188959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:00.908733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.648280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.355385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:05.964443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.749562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:13:47.944587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:13:51.107096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.605027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.220009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:55.862859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:57.728845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.291017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.046022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.761029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.459427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.084555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:07.870157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:09.545390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:13:48.036695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:49.515817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:51.202420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.695970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.345871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.000081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:13:59.406506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.160168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.869574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.556222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.208599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:13:51.293944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.790270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.465440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.120334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:57.926696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.523956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.265332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:02.959597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.652729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.363603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.063169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:10.094579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:46.812394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.217550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:49.711725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:51.381208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.868573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.563409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.226843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.048042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.646790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.391059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.064345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.742765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.463914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.164060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:10.219550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:46.941575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:48.320287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:49.809915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:51.474493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:52.955800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:54.659037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:56.339534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:58.165623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:13:59.775653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:01.497838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:03.153589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:04.866940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:06.588225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:08.269790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:14:18.600883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름하천형식높이(미터)길이(미터)정상표고(EL_미터)체적(천미터세제곱)유역면적(킬로미터제곱)연간용수공급량(백만미터세제곱)저수면적(킬로미터제곱)계획홍수위(EL_미터)상시만수위(EL_미터)홍수기제한수위(EL_미터)월류정표고(EL_미터)저수위(EL_미터)사업시작일사업종료일총저수용량(백만미터세제곱)유효저수용량(백만미터세제곱)홍수조절용량(백만미터세제곱)
댐이름1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
하천1.0001.0000.8090.6630.0000.0000.0000.0000.0000.0000.1880.0000.0000.0000.0000.9900.9800.0000.0000.000
형식1.0000.8091.0000.2350.0000.0000.0000.5190.0000.0000.6200.0000.0000.0000.0000.9560.9250.0000.0000.000
높이(미터)1.0000.6630.2351.0000.6390.3340.6820.6050.6560.4670.3340.0000.0000.0000.0000.0000.0000.7170.5190.549
길이(미터)1.0000.0000.0000.6391.0000.0490.6230.7240.5240.3310.0000.0000.0000.0000.0000.4070.9520.6350.3290.333
정상표고(EL_미터)1.0000.0000.0000.3340.0491.0000.0000.0000.0000.0001.0000.9970.9980.9910.9760.0000.0000.0000.1620.319
체적(천미터세제곱)1.0000.0000.0000.6820.6230.0001.0000.6970.6870.6540.0000.0000.0000.0000.3610.0000.0000.6950.7040.677
유역면적(킬로미터제곱)1.0000.0000.5190.6050.7240.0000.6971.0000.9000.8570.0000.0000.0000.0000.0000.0000.0000.4320.5620.771
연간용수공급량(백만미터세제곱)1.0000.0000.0000.6560.5240.0000.6870.9001.0000.9510.0000.0000.2290.0000.0000.0000.0000.9720.8840.961
저수면적(킬로미터제곱)1.0000.0000.0000.4670.3310.0000.6540.8570.9511.0000.0000.0000.5340.0000.3420.0000.0000.8810.9740.889
계획홍수위(EL_미터)1.0000.1880.6200.3340.0001.0000.0000.0000.0000.0001.0000.9970.9980.9910.9760.7630.8340.3180.1620.319
상시만수위(EL_미터)1.0000.0000.0000.0000.0000.9970.0000.0000.0000.0000.9971.0001.0000.9900.9740.0000.5390.0000.1230.341
홍수기제한수위(EL_미터)1.0000.0000.0000.0000.0000.9980.0000.0000.2290.5340.9981.0001.0000.9980.9760.0000.0000.5160.3850.459
월류정표고(EL_미터)1.0000.0000.0000.0000.0000.9910.0000.0000.0000.0000.9910.9900.9981.0000.9850.0000.0000.0000.2450.622
저수위(EL_미터)1.0000.0000.0000.0000.0000.9760.3610.0000.0000.3420.9760.9740.9760.9851.0000.0000.0000.3670.5590.824
사업시작일1.0000.9900.9560.0000.4070.0000.0000.0000.0000.0000.7630.0000.0000.0000.0001.0000.9930.5720.0000.546
사업종료일1.0000.9800.9250.0000.9520.0000.0000.0000.0000.0000.8340.5390.0000.0000.0000.9931.0000.0000.0000.000
총저수용량(백만미터세제곱)1.0000.0000.0000.7170.6350.0000.6950.4320.9720.8810.3180.0000.5160.0000.3670.5720.0001.0000.9420.887
유효저수용량(백만미터세제곱)1.0000.0000.0000.5190.3290.1620.7040.5620.8840.9740.1620.1230.3850.2450.5590.0000.0000.9421.0000.877
홍수조절용량(백만미터세제곱)1.0000.0000.0000.5490.3330.3190.6770.7710.9610.8890.3190.3410.4590.6220.8240.5460.0000.8870.8771.000
2023-12-12T16:14:18.863850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
높이(미터)길이(미터)정상표고(EL_미터)체적(천미터세제곱)유역면적(킬로미터제곱)연간용수공급량(백만미터세제곱)저수면적(킬로미터제곱)계획홍수위(EL_미터)상시만수위(EL_미터)홍수기제한수위(EL_미터)월류정표고(EL_미터)저수위(EL_미터)총저수용량(백만미터세제곱)유효저수용량(백만미터세제곱)홍수조절용량(백만미터세제곱)형식
높이(미터)1.0000.4830.6510.7230.6120.5900.5710.6470.4310.3920.5880.5040.7380.6880.6390.113
길이(미터)0.4831.0000.2030.6750.5610.4520.4350.0370.0880.0480.1960.2170.5980.4840.5560.000
정상표고(EL_미터)0.6510.2031.0000.3980.3390.3600.3360.9990.8360.7910.9760.9740.4450.4230.3500.000
체적(천미터세제곱)0.7230.6750.3981.0000.6970.6970.6310.3960.3230.3640.4340.3610.7470.5550.4740.000
유역면적(킬로미터제곱)0.6120.5610.3390.6971.0000.7260.764-0.3540.2380.2860.3580.3460.4790.6840.7100.276
연간용수공급량(백만미터세제곱)0.5900.4520.3600.6970.7261.0000.9390.3590.5690.6820.4800.4140.7850.7780.6650.000
저수면적(킬로미터제곱)0.5710.4350.3360.6310.7640.9391.0000.3310.4810.5630.3800.3130.7840.8950.7590.000
계획홍수위(EL_미터)0.6470.0370.9990.396-0.3540.3590.3311.0000.8420.8020.9770.9750.3800.4200.3470.263
상시만수위(EL_미터)0.4310.0880.8360.3230.2380.5690.4810.8421.0000.9980.9150.8720.3200.4450.2980.000
홍수기제한수위(EL_미터)0.3920.0480.7910.3640.2860.6820.5630.8020.9981.0000.8770.8440.3380.4870.3290.000
월류정표고(EL_미터)0.5880.1960.9760.4340.3580.4800.3800.9770.9150.8771.0000.9740.4340.3750.2700.000
저수위(EL_미터)0.5040.2170.9740.3610.3460.4140.3130.9750.8720.8440.9741.0000.4300.3350.2670.000
총저수용량(백만미터세제곱)0.7380.5980.4450.7470.4790.7850.7840.3800.3200.3380.4340.4301.0000.8030.7720.000
유효저수용량(백만미터세제곱)0.6880.4840.4230.5550.6840.7780.8950.4200.4450.4870.3750.3350.8031.0000.8730.000
홍수조절용량(백만미터세제곱)0.6390.5560.3500.4740.7100.6650.7590.3470.2980.3290.2700.2670.7720.8731.0000.000
형식0.1130.0000.0000.0000.2760.0000.0000.2630.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T16:14:10.398526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:14:10.709512image/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.
2023-12-12T16:14:10.948363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

댐이름하천형식높이(미터)길이(미터)정상표고(EL_미터)체적(천미터세제곱)유역면적(킬로미터제곱)연간용수공급량(백만미터세제곱)저수면적(킬로미터제곱)계획홍수위(EL_미터)상시만수위(EL_미터)홍수기제한수위(EL_미터)월류정표고(EL_미터)저수위(EL_미터)사업시작일사업종료일총저수용량(백만미터세제곱)유효저수용량(백만미터세제곱)홍수조절용량(백만미터세제곱)
0낙동강하굿둑낙동강C.G_E.C.R.D18.72230.09.2220623560750<NA>3.7<NA><NA><NA><NA>1983-09-011990-06-0129700
1주암(조)이사천E.C.R.D99.9562.6115.049651352197.8111.1108.5108.5108.560.01984-09-011992-12-0125021020
2횡성섬강E.C.R.D48.5205.0184.06752091205.8180.0180.0178.2167.0160.01990-01-012002-11-01877310
3주암(본)보성강E.C.R.D58.0330.0115.01573101027033.0110.5108.5<NA>98.585.01984-09-011992-12-0145735260
4부안직소천C.F.R.D50.0282.049.061459353.043.841.241.241.223.01990-02-011996-12-0150369
5군남임진강C.G.D26.0658.045.0177419103.040.031.023.031.023.02005-11-012013-12-01727171
6군위위천C.F.R.D45.0390.0208.487788382.7205.1204.0<NA>197.2181.02000-01-012012-12-0149403
7소양강북한강E.C.R.D123.0530.0203.095912703121370.0198.0193.5190.3185.5150.01967-04-011973-12-0129001900500
8충주남한강C.G.D97.5447.0147.59026648338097.0145.0141.0138.0126.0110.01978-06-011986-10-0127501789616
9평화의댐(3단계)북한강C.F.R.D125.0601.0270.07905320800.0264.60.00.0160.0160.02012-06-012018-12-01263000
댐이름하천형식높이(미터)길이(미터)정상표고(EL_미터)체적(천미터세제곱)유역면적(킬로미터제곱)연간용수공급량(백만미터세제곱)저수면적(킬로미터제곱)계획홍수위(EL_미터)상시만수위(EL_미터)홍수기제한수위(EL_미터)월류정표고(EL_미터)저수위(EL_미터)사업시작일사업종료일총저수용량(백만미터세제곱)유효저수용량(백만미터세제곱)홍수조절용량(백만미터세제곱)
51합천창녕보낙동강C.G.W<NA>328.0<NA><NA>15074<NA><NA>18.6<NA><NA><NA><NA>2009-11-012013-01-0170<NA><NA>
52창녕함안보낙동강C.G.W<NA>549.0<NA><NA>20697<NA><NA>13.7<NA><NA><NA><NA>2009-11-012012-10-01101<NA><NA>
53강천보한강C.G.W<NA>440.0<NA><NA>10972<NA><NA>45.1<NA><NA><NA><NA>2009-11-012012-10-019<NA><NA>
54여주보한강C.G.W<NA>513.0<NA><NA>11115<NA><NA>39.6<NA><NA><NA><NA>2009-11-012012-10-0111<NA><NA>
55이포보한강C.G.W<NA>521.0<NA><NA>11803<NA><NA>36.2<NA><NA><NA><NA>2009-11-012012-10-0114<NA><NA>
56세종보금강C.G.W<NA>348.0<NA><NA>6942<NA><NA>23.5<NA><NA><NA><NA>2009-05-012012-06-016<NA><NA>
57공주보금강C.G.W<NA>280.0<NA><NA>7408<NA><NA>17.6<NA><NA><NA><NA>2009-11-012012-10-0116<NA><NA>
58백제보금강C.G.W<NA>311.0<NA><NA>7976<NA><NA>13.1<NA><NA><NA><NA>2009-11-012012-10-0124<NA><NA>
59승촌보영산강C.G.W<NA>512.0<NA><NA>1327<NA><NA>11.6<NA><NA><NA><NA>2009-10-012012-10-019<NA><NA>
60죽산보영산강C.G.W<NA>184.0<NA><NA>2359<NA><NA>7.1<NA><NA><NA><NA>2009-11-012012-10-0126<NA><NA>