Overview

Dataset statistics

Number of variables8
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory74.8 B

Variable types

Numeric7
Text1

Dataset

DescriptionKwater가 운영하고 있는 다목적댐, 용수댐에 대한 시설제원정보 (DAMCD 댐코드, DAMNM 댐명, HG 높이, LT 길이, BASINARA 유역면적, PLFWL 계획홍수위, NHWL 상시만수위, TOTRSQTY 총저수량)
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049846/fileData.do

Alerts

댐코드 is highly overall correlated with 계획 홍수위 and 1 other fieldsHigh correlation
높이 is highly overall correlated with 길이 and 4 other fieldsHigh correlation
길이 is highly overall correlated with 높이 and 2 other fieldsHigh correlation
유역면적 is highly overall correlated with 높이 and 2 other fieldsHigh correlation
계획 홍수위 is highly overall correlated with 댐코드 and 2 other fieldsHigh correlation
상시 만수위 is highly overall correlated with 댐코드 and 2 other fieldsHigh correlation
총 저수량 is highly overall correlated with 높이 and 2 other fieldsHigh correlation
댐코드 has unique valuesUnique
댐명 has unique valuesUnique
유역면적 has unique valuesUnique
계획 홍수위 has unique valuesUnique
총 저수량 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:57:37.558288
Analysis finished2023-12-12 13:57:42.588333
Duration5.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2477278.8
Minimum1001210
Maximum5101110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:57:42.677365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001210
5-th percentile1005210
Q12006101
median2101210
Q33004610
95-th percentile4374307.3
Maximum5101110
Range4099900
Interquartile range (IQR)998509

Descriptive statistics

Standard deviation1057897.5
Coefficient of variation (CV)0.42704016
Kurtosis0.50184804
Mean2477278.8
Median Absolute Deviation (MAD)301991
Skewness0.93345619
Sum86704758
Variance1.1191472 × 1012
MonotonicityStrictly increasing
2023-12-12T22:57:42.858566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1001210 1
 
2.9%
1003110 1
 
2.9%
2201231 1
 
2.9%
2301210 1
 
2.9%
2403201 1
 
2.9%
2503210 1
 
2.9%
2503220 1
 
2.9%
3001110 1
 
2.9%
3008110 1
 
2.9%
3203110 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1001210 1
2.9%
1003110 1
2.9%
1006110 1
2.9%
1012110 1
2.9%
1302210 1
2.9%
2001110 1
2.9%
2002110 1
2.9%
2002111 1
2.9%
2004101 1
2.9%
2008101 1
2.9%
ValueCountFrequency (%)
5101110 1
2.9%
5002201 1
2.9%
4105210 1
2.9%
4104610 1
2.9%
4007110 1
2.9%
4001110 1
2.9%
3303110 1
2.9%
3203110 1
2.9%
3008110 1
2.9%
3001110 1
2.9%

댐명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T22:57:43.111176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2857143
Min length3

Characters and Unicode

Total characters115
Distinct characters58
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row광동댐
2nd row충주댐
3rd row횡성댐
4th row소양강댐
5th row달방댐
ValueCountFrequency (%)
광동댐 1
 
2.9%
사연댐 1
 
2.9%
대곡댐 1
 
2.9%
선암댐 1
 
2.9%
감포댐 1
 
2.9%
연초댐 1
 
2.9%
구천댐 1
 
2.9%
용담댐 1
 
2.9%
대암댐 1
 
2.9%
대청댐 1
 
2.9%
Other values (25) 25
71.4%
2023-12-12T22:57:43.523571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
댐 34
29.6%
천 4
 
3.5%
암 4
 
3.5%
주 4
 
3.5%
안 3
 
2.6%
대 3
 
2.6%
강 3
 
2.6%
보 2
 
1.7%
( 2
 
1.7%
영 2
 
1.7%
Other values (48) 54
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111
96.5%
Open Punctuation 2
 
1.7%
Close Punctuation 2
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
댐 34
30.6%
천 4
 
3.6%
암 4
 
3.6%
주 4
 
3.6%
안 3
 
2.7%
대 3
 
2.7%
강 3
 
2.7%
보 2
 
1.8%
영 2
 
1.8%
연 2
 
1.8%
Other values (46) 50
45.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111
96.5%
Common 4
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
댐 34
30.6%
천 4
 
3.6%
암 4
 
3.6%
주 4
 
3.6%
안 3
 
2.7%
대 3
 
2.7%
강 3
 
2.7%
보 2
 
1.8%
영 2
 
1.8%
연 2
 
1.8%
Other values (46) 50
45.0%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111
96.5%
ASCII 4
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
댐 34
30.6%
천 4
 
3.6%
암 4
 
3.6%
주 4
 
3.6%
안 3
 
2.7%
대 3
 
2.7%
강 3
 
2.7%
보 2
 
1.8%
영 2
 
1.8%
연 2
 
1.8%
Other values (46) 50
45.0%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

높이
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.688571
Minimum22
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:57:43.687945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26.25
Q143.5
median53.5
Q368.5
95-th percentile98.22
Maximum123
Range101
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.074888
Coefficient of variation (CV)0.39999063
Kurtosis0.75736514
Mean57.688571
Median Absolute Deviation (MAD)13.5
Skewness0.89651916
Sum2019.1
Variance532.45045
MonotonicityNot monotonic
2023-12-12T22:57:43.818503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
50.0 3
 
8.6%
58.5 2
 
5.7%
39.5 1
 
2.9%
72.0 1
 
2.9%
52.0 1
 
2.9%
22.0 1
 
2.9%
35.0 1
 
2.9%
24.5 1
 
2.9%
70.0 1
 
2.9%
64.0 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
22.0 1
2.9%
24.5 1
2.9%
27.0 1
2.9%
32.5 1
2.9%
34.0 1
2.9%
35.0 1
2.9%
37.3 1
2.9%
39.5 1
2.9%
42.0 1
2.9%
45.0 1
2.9%
ValueCountFrequency (%)
123.0 1
2.9%
99.9 1
2.9%
97.5 1
2.9%
96.0 1
2.9%
89.0 1
2.9%
83.0 1
2.9%
73.0 1
2.9%
72.0 1
2.9%
70.0 1
2.9%
67.0 1
2.9%

길이
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean382.25143
Minimum108
Maximum1126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:57:43.977324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile169
Q1286.5
median344.2
Q3459.5
95-th percentile577.42
Maximum1126
Range1018
Interquartile range (IQR)173

Descriptive statistics

Standard deviation179.07766
Coefficient of variation (CV)0.46848134
Kurtosis7.8499975
Mean382.25143
Median Absolute Deviation (MAD)94.2
Skewness2.0441846
Sum13378.8
Variance32068.808
MonotonicityNot monotonic
2023-12-12T22:57:44.119606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
300.0 2
 
5.7%
292.0 1
 
2.9%
318.0 1
 
2.9%
331.0 1
 
2.9%
108.0 1
 
2.9%
120.0 1
 
2.9%
234.0 1
 
2.9%
498.0 1
 
2.9%
495.0 1
 
2.9%
291.0 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
108.0 1
2.9%
120.0 1
2.9%
190.0 1
2.9%
205.0 1
2.9%
223.5 1
2.9%
234.0 1
2.9%
250.0 1
2.9%
274.0 1
2.9%
282.0 1
2.9%
291.0 1
2.9%
ValueCountFrequency (%)
1126.0 1
2.9%
612.0 1
2.9%
562.6 1
2.9%
535.0 1
2.9%
530.0 1
2.9%
515.0 1
2.9%
498.0 1
2.9%
495.0 1
2.9%
472.0 1
2.9%
447.0 1
2.9%

유역면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean685.94486
Minimum1.2
Maximum6648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:57:44.239364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile5.791
Q145.15
median125
Q3844
95-th percentile2853.3
Maximum6648
Range6646.8
Interquartile range (IQR)798.85

Descriptive statistics

Standard deviation1314.9573
Coefficient of variation (CV)1.9170015
Kurtosis12.339633
Mean685.94486
Median Absolute Deviation (MAD)110
Skewness3.2360075
Sum24008.07
Variance1729112.7
MonotonicityNot monotonic
2023-12-12T22:57:44.660750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
125.0 1
 
2.9%
6648.0 1
 
2.9%
57.5 1
 
2.9%
1.2 1
 
2.9%
3.67 1
 
2.9%
11.7 1
 
2.9%
12.7 1
 
2.9%
930.0 1
 
2.9%
3204.0 1
 
2.9%
163.6 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1.2 1
2.9%
3.67 1
2.9%
6.7 1
2.9%
11.7 1
2.9%
12.7 1
2.9%
19.9 1
2.9%
29.4 1
2.9%
32.6 1
2.9%
41.3 1
2.9%
49.0 1
2.9%
ValueCountFrequency (%)
6648.0 1
2.9%
3204.0 1
2.9%
2703.0 1
2.9%
2285.0 1
2.9%
1584.0 1
2.9%
1361.0 1
2.9%
1010.0 1
2.9%
930.0 1
2.9%
925.0 1
2.9%
763.0 1
2.9%

계획 홍수위
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.59057
Minimum30
Maximum675.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:57:44.799033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile43.02
Q170.85
median122.7
Q3188.85
95-th percentile295.32
Maximum675.3
Range645.3
Interquartile range (IQR)118

Descriptive statistics

Standard deviation118.48275
Coefficient of variation (CV)0.7973773
Kurtosis11.049628
Mean148.59057
Median Absolute Deviation (MAD)57.3
Skewness2.790043
Sum5200.67
Variance14038.162
MonotonicityNot monotonic
2023-12-12T22:57:44.954518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
675.3 1
 
2.9%
145.0 1
 
2.9%
122.7 1
 
2.9%
30.0 1
 
2.9%
41.2 1
 
2.9%
49.6 1
 
2.9%
94.54 1
 
2.9%
265.5 1
 
2.9%
80.0 1
 
2.9%
75.5 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
30.0 1
2.9%
41.2 1
2.9%
43.8 1
2.9%
44.97 1
2.9%
46.0 1
2.9%
49.6 1
2.9%
52.91 1
2.9%
63.2 1
2.9%
66.2 1
2.9%
75.5 1
2.9%
ValueCountFrequency (%)
675.3 1
2.9%
364.9 1
2.9%
265.5 1
2.9%
238.5 1
2.9%
210.2 1
2.9%
205.1 1
2.9%
198.6 1
2.9%
198.0 1
2.9%
197.7 1
2.9%
180.0 1
2.9%

상시 만수위
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.29429
Minimum30
Maximum672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:57:45.099320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40.7
Q169
median120
Q3186.75
95-th percentile293.65
Maximum672
Range642
Interquartile range (IQR)117.75

Descriptive statistics

Standard deviation118.39365
Coefficient of variation (CV)0.80928419
Kurtosis10.997438
Mean146.29429
Median Absolute Deviation (MAD)60
Skewness2.7851274
Sum5120.3
Variance14017.057
MonotonicityNot monotonic
2023-12-12T22:57:45.269183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
108.5 2
 
5.7%
672.0 1
 
2.9%
76.5 1
 
2.9%
120.0 1
 
2.9%
30.0 1
 
2.9%
40.0 1
 
2.9%
48.0 1
 
2.9%
93.0 1
 
2.9%
263.5 1
 
2.9%
74.0 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
30.0 1
2.9%
40.0 1
2.9%
41.0 1
2.9%
41.2 1
2.9%
43.9 1
2.9%
48.0 1
2.9%
48.5 1
2.9%
60.0 1
2.9%
64.0 1
2.9%
74.0 1
2.9%
ValueCountFrequency (%)
672.0 1
2.9%
364.0 1
2.9%
263.5 1
2.9%
236.0 1
2.9%
207.2 1
2.9%
204.0 1
2.9%
196.5 1
2.9%
195.0 1
2.9%
193.5 1
2.9%
180.0 1
2.9%

총 저수량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.93943
Minimum2.02
Maximum2900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:57:45.434202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.02
5-th percentile4.446
Q120.285
median73.6
Q3383.1
95-th percentile1868
Maximum2900
Range2897.98
Interquartile range (IQR)362.815

Descriptive statistics

Standard deviation707.44814
Coefficient of variation (CV)1.8522522
Kurtosis7.0983245
Mean381.93943
Median Absolute Deviation (MAD)64.85
Skewness2.6869669
Sum13367.88
Variance500482.87
MonotonicityNot monotonic
2023-12-12T22:57:45.582973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
13.13 1
 
2.9%
2750.0 1
 
2.9%
36.16 1
 
2.9%
2.02 1
 
2.9%
2.64 1
 
2.9%
5.22 1
 
2.9%
10.02 1
 
2.9%
815.0 1
 
2.9%
1490.0 1
 
2.9%
116.9 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
2.02 1
2.9%
2.64 1
2.9%
5.22 1
2.9%
8.75 1
2.9%
10.02 1
2.9%
10.26 1
2.9%
13.13 1
2.9%
13.14 1
2.9%
18.46 1
2.9%
22.11 1
2.9%
ValueCountFrequency (%)
2900.0 1
2.9%
2750.0 1
2.9%
1490.0 1
2.9%
1248.0 1
2.9%
815.0 1
2.9%
790.0 1
2.9%
595.0 1
2.9%
466.0 1
2.9%
457.0 1
2.9%
309.2 1
2.9%

Interactions

2023-12-12T22:57:41.498921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:37.792938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.663053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.201020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.755736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.337635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.924419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.589765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:37.869581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.750847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.270880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.829091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.421083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.009692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.707698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:37.947951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.824664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.349618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.908290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.506974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.086347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.818944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.309810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.894885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.412591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.977909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.578288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.167861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.926147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.405809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.977595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.489609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.068220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.654846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.241721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:42.071462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.504575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.052428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.577809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.155911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.734617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.320084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:42.196575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:38.583751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.128287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:39.674725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.259511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:40.829776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:57:41.409211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:57:45.714785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐코드댐명높이길이유역면적계획 홍수위상시 만수위총 저수량
댐코드1.0001.0000.4710.2010.2720.1530.2490.558
댐명1.0001.0001.0001.0001.0001.0001.0001.000
높이0.4711.0001.0000.7110.7550.0000.0000.727
길이0.2011.0000.7111.0000.8410.0000.0000.769
유역면적0.2721.0000.7550.8411.0000.0000.0000.964
계획 홍수위0.1531.0000.0000.0000.0001.0000.9990.000
상시 만수위0.2491.0000.0000.0000.0000.9991.0000.000
총 저수량0.5581.0000.7270.7690.9640.0000.0001.000
2023-12-12T22:57:45.856939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐코드높이길이유역면적계획 홍수위상시 만수위총 저수량
댐코드1.000-0.133-0.065-0.240-0.529-0.512-0.101
높이-0.1331.0000.6220.5900.5160.5150.725
길이-0.0650.6221.0000.6780.2380.2150.702
유역면적-0.2400.5900.6781.0000.3420.3310.945
계획 홍수위-0.5290.5160.2380.3421.0000.9980.319
상시 만수위-0.5120.5150.2150.3310.9981.0000.313
총 저수량-0.1010.7250.7020.9450.3190.3131.000

Missing values

2023-12-12T22:57:42.350064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:57:42.514879image/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

댐코드댐명높이길이유역면적계획 홍수위상시 만수위총 저수량
01001210광동댐39.5292.0125.0675.3672.013.13
11003110충주댐97.5447.06648.0145.0141.02750.0
21006110횡성댐48.5205.0209.0180.0180.086.9
31012110소양강댐123.0530.02703.0198.0193.52900.0
41302210달방댐53.5326.029.4114.25112.08.75
52001110안동댐83.0612.01584.0161.7160.01248.0
62002110임하댐73.0515.01361.0164.7163.0595.0
72002111성덕댐58.5274.041.3364.9364.027.9
82004101영주댐55.5400.0500.0164.0161.0181.1
92008101군위댐45.0390.087.5205.1204.048.7
댐코드댐명높이길이유역면적계획 홍수위상시 만수위총 저수량
253001110용담댐70.0498.0930.0265.5263.5815.0
263008110대청댐72.0495.03204.080.076.51490.0
273203110보령댐50.0291.0163.675.574.0116.9
283303110부안댐50.0282.059.043.841.250.3
294001110섬진강댐64.0344.2763.0197.7196.5466.0
304007110주암(본)댐58.0330.01010.0110.5108.5457.0
314104610주암(조)99.9562.6134.6111.1108.5250.0
324105210수어댐67.0437.049.066.264.031.27
335002201평림댐37.3390.519.9111.3109.710.26
345101110장흥댐53.0403.0193.082.882.0191.0