Overview

Dataset statistics

Number of variables15
Number of observations1798
Missing cells19
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory228.4 KiB
Average record size in memory130.1 B

Variable types

Categorical2
Text3
Numeric10

Dataset

Description전북특별자치도 도내 저수지 현황(저수지명, 위치, 수혜면적, 유역면적, 만수면적, 총저수량, 사수량, 유효저수량, 제당형식 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/3081296/fileData.do

Alerts

유 역면 적 is highly overall correlated with 총저수량 and 1 other fieldsHigh correlation
만 수면 적 is highly overall correlated with 총저수량 and 1 other fieldsHigh correlation
총저수량 is highly overall correlated with 유 역면 적 and 2 other fieldsHigh correlation
유효저수량 is highly overall correlated with 유 역면 적 and 2 other fieldsHigh correlation
시군별 is highly overall correlated with 제당형 식High correlation
제당형 식 is highly overall correlated with 시군별High correlation
수 혜면 적 has 23 (1.3%) zerosZeros
사수량 has 486 (27.0%) zerosZeros

Reproduction

Analysis started2024-03-14 11:47:42.100474
Analysis finished2024-03-14 11:48:09.222722
Duration27.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군별
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
고창군
310 
남원시
220 
정읍시
193 
임실군
158 
완주군
149 
Other values (9)
768 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고창군 310
17.2%
남원시 220
12.2%
정읍시 193
10.7%
임실군 158
8.8%
완주군 149
8.3%
순창군 136
7.6%
진안군 116
 
6.5%
익산시 107
 
6.0%
김제시 103
 
5.7%
군산시 75
 
4.2%
Other values (4) 231
12.8%

Length

2024-03-14T20:48:09.335485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고창군 310
17.2%
남원시 220
12.2%
정읍시 193
10.7%
임실군 158
8.8%
완주군 149
8.3%
순창군 136
7.6%
진안군 116
 
6.5%
익산시 107
 
6.0%
김제시 103
 
5.7%
군산시 75
 
4.2%
Other values (4) 231
12.8%
Distinct1451
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-14T20:48:10.760358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.1373749
Min length3

Characters and Unicode

Total characters5641
Distinct characters318
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1236 ?
Unique (%)68.7%

Sample

1st row장천제
2nd row작지제
3rd row비아제
4th row우묵제
5th row외절제
ValueCountFrequency (%)
24
 
1.2%
신기제 9
 
0.5%
1 8
 
0.4%
2 7
 
0.4%
고산제 7
 
0.4%
내동제 7
 
0.4%
7
 
0.4%
신촌제 7
 
0.4%
6
 
0.3%
6
 
0.3%
Other values (1428) 1838
95.4%
2024-03-14T20:48:12.619961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1695
30.0%
201
 
3.6%
177
 
3.1%
149
 
2.6%
129
 
2.3%
93
 
1.6%
67
 
1.2%
66
 
1.2%
66
 
1.2%
64
 
1.1%
Other values (308) 2934
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5342
94.7%
Space Separator 201
 
3.6%
Decimal Number 92
 
1.6%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1695
31.7%
177
 
3.3%
149
 
2.8%
129
 
2.4%
93
 
1.7%
67
 
1.3%
66
 
1.2%
66
 
1.2%
64
 
1.2%
58
 
1.1%
Other values (301) 2778
52.0%
Decimal Number
ValueCountFrequency (%)
2 45
48.9%
1 41
44.6%
3 4
 
4.3%
4 2
 
2.2%
Space Separator
ValueCountFrequency (%)
201
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5342
94.7%
Common 299
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1695
31.7%
177
 
3.3%
149
 
2.8%
129
 
2.4%
93
 
1.7%
67
 
1.3%
66
 
1.2%
66
 
1.2%
64
 
1.2%
58
 
1.1%
Other values (301) 2778
52.0%
Common
ValueCountFrequency (%)
201
67.2%
2 45
 
15.1%
1 41
 
13.7%
3 4
 
1.3%
( 3
 
1.0%
) 3
 
1.0%
4 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5342
94.7%
ASCII 299
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1695
31.7%
177
 
3.3%
149
 
2.8%
129
 
2.4%
93
 
1.7%
67
 
1.3%
66
 
1.2%
66
 
1.2%
64
 
1.2%
58
 
1.1%
Other values (301) 2778
52.0%
ASCII
ValueCountFrequency (%)
201
67.2%
2 45
 
15.1%
1 41
 
13.7%
3 4
 
1.3%
( 3
 
1.0%
) 3
 
1.0%
4 2
 
0.7%

읍면
Text

Distinct162
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-14T20:48:14.100038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1023359
Min length1

Characters and Unicode

Total characters3780
Distinct characters126
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

Unique10 ?
Unique (%)0.6%

Sample

1st row완산
2nd row완산
3rd row완산
4th row완산
5th row완산
ValueCountFrequency (%)
대산 45
 
2.5%
성수 41
 
2.3%
공음 34
 
1.9%
용지 32
 
1.8%
진안 30
 
1.7%
무장 27
 
1.5%
소양 27
 
1.5%
성송 26
 
1.4%
고창 26
 
1.4%
성내 26
 
1.4%
Other values (152) 1484
82.5%
2024-03-14T20:48:15.742750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
258
 
6.8%
152
 
4.0%
137
 
3.6%
132
 
3.5%
106
 
2.8%
102
 
2.7%
86
 
2.3%
76
 
2.0%
75
 
2.0%
75
 
2.0%
Other values (116) 2581
68.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3762
99.5%
Space Separator 16
 
0.4%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
258
 
6.9%
152
 
4.0%
137
 
3.6%
132
 
3.5%
106
 
2.8%
102
 
2.7%
86
 
2.3%
76
 
2.0%
75
 
2.0%
75
 
2.0%
Other values (114) 2563
68.1%
Space Separator
ValueCountFrequency (%)
16
100.0%
Decimal Number
ValueCountFrequency (%)
3 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3762
99.5%
Common 18
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
258
 
6.9%
152
 
4.0%
137
 
3.6%
132
 
3.5%
106
 
2.8%
102
 
2.7%
86
 
2.3%
76
 
2.0%
75
 
2.0%
75
 
2.0%
Other values (114) 2563
68.1%
Common
ValueCountFrequency (%)
16
88.9%
3 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3762
99.5%
ASCII 18
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
258
 
6.9%
152
 
4.0%
137
 
3.6%
132
 
3.5%
106
 
2.8%
102
 
2.7%
86
 
2.3%
76
 
2.0%
75
 
2.0%
75
 
2.0%
Other values (114) 2563
68.1%
ASCII
ValueCountFrequency (%)
16
88.9%
3 2
 
11.1%

리동
Text

Distinct767
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-14T20:48:17.407542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.041713
Min length1

Characters and Unicode

Total characters3671
Distinct characters219
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

Unique309 ?
Unique (%)17.2%

Sample

1st row평화
2nd row평화
3rd row삼천
4th row삼천
5th row삼천
ValueCountFrequency (%)
삼천 15
 
0.8%
금평 13
 
0.7%
덕천 12
 
0.7%
대신 10
 
0.6%
덕산 9
 
0.5%
반월 9
 
0.5%
내월 8
 
0.4%
금성 8
 
0.4%
남산 8
 
0.4%
명덕 8
 
0.4%
Other values (756) 1698
94.4%
2024-03-14T20:48:19.522360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200
 
5.4%
109
 
3.0%
103
 
2.8%
81
 
2.2%
81
 
2.2%
79
 
2.2%
78
 
2.1%
76
 
2.1%
76
 
2.1%
74
 
2.0%
Other values (209) 2714
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3669
99.9%
Space Separator 1
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
200
 
5.5%
109
 
3.0%
103
 
2.8%
81
 
2.2%
81
 
2.2%
79
 
2.2%
78
 
2.1%
76
 
2.1%
76
 
2.1%
74
 
2.0%
Other values (207) 2712
73.9%
Space Separator
ValueCountFrequency (%)
1
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3669
99.9%
Common 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
200
 
5.5%
109
 
3.0%
103
 
2.8%
81
 
2.2%
81
 
2.2%
79
 
2.2%
78
 
2.1%
76
 
2.1%
76
 
2.1%
74
 
2.0%
Other values (207) 2712
73.9%
Common
ValueCountFrequency (%)
1
50.0%
3 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3669
99.9%
ASCII 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
200
 
5.5%
109
 
3.0%
103
 
2.8%
81
 
2.2%
81
 
2.2%
79
 
2.2%
78
 
2.1%
76
 
2.1%
76
 
2.1%
74
 
2.0%
Other values (207) 2712
73.9%
ASCII
ValueCountFrequency (%)
1
50.0%
3 1
50.0%

수 혜면 적
Real number (ℝ)

ZEROS 

Distinct225
Distinct (%)12.5%
Missing3
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean8.0746685
Minimum0
Maximum129
Zeros23
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:19.774005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5.5
Q310
95-th percentile23.06
Maximum129
Range129
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.9190565
Coefficient of variation (CV)1.1045725
Kurtosis37.141507
Mean8.0746685
Median Absolute Deviation (MAD)2.9
Skewness4.5588189
Sum14494.03
Variance79.549568
MonotonicityNot monotonic
2024-03-14T20:48:20.034144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 99
 
5.5%
3.0 93
 
5.2%
4.0 75
 
4.2%
2.0 73
 
4.1%
7.0 71
 
3.9%
8.0 66
 
3.7%
10.0 58
 
3.2%
1.0 57
 
3.2%
6.0 53
 
2.9%
3.5 36
 
2.0%
Other values (215) 1114
62.0%
ValueCountFrequency (%)
0.0 23
1.3%
0.3 1
 
0.1%
0.4 1
 
0.1%
0.5 7
 
0.4%
0.6 2
 
0.1%
0.7 3
 
0.2%
0.8 4
 
0.2%
0.9 3
 
0.2%
1.0 57
3.2%
1.1 5
 
0.3%
ValueCountFrequency (%)
129.0 1
0.1%
97.0 1
0.1%
89.1 1
0.1%
88.0 1
0.1%
72.0 1
0.1%
69.0 1
0.1%
61.0 1
0.1%
60.0 1
0.1%
49.5 1
0.1%
48.2 1
0.1%

유 역면 적
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.8651
Minimum1
Maximum599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:20.371699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q114
median26
Q349.75
95-th percentile121
Maximum599
Range598
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation50.222631
Coefficient of variation (CV)1.2289859
Kurtosis32.034308
Mean40.8651
Median Absolute Deviation (MAD)15
Skewness4.6107871
Sum73475.45
Variance2522.3126
MonotonicityNot monotonic
2024-03-14T20:48:20.616361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 65
 
3.6%
20.0 59
 
3.3%
13.0 51
 
2.8%
8.0 45
 
2.5%
18.0 45
 
2.5%
14.0 44
 
2.4%
16.0 43
 
2.4%
40.0 41
 
2.3%
25.0 40
 
2.2%
12.0 39
 
2.2%
Other values (207) 1326
73.7%
ValueCountFrequency (%)
1.0 2
 
0.1%
1.5 2
 
0.1%
2.0 9
 
0.5%
2.5 1
 
0.1%
3.0 22
1.2%
3.8 1
 
0.1%
4.0 30
1.7%
5.0 30
1.7%
5.4 1
 
0.1%
6.0 25
1.4%
ValueCountFrequency (%)
599.0 1
0.1%
518.0 1
0.1%
487.0 1
0.1%
480.0 1
0.1%
450.0 1
0.1%
425.0 1
0.1%
410.0 1
0.1%
380.0 2
0.1%
373.0 1
0.1%
352.0 1
0.1%

만 수면 적
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)6.5%
Missing11
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.4429771
Minimum0
Maximum52
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:20.867453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.8
Q31.3
95-th percentile3.47
Maximum52
Range52
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation3.4376021
Coefficient of variation (CV)2.3822985
Kurtosis93.366501
Mean1.4429771
Median Absolute Deviation (MAD)0.4
Skewness8.7924376
Sum2578.6
Variance11.817108
MonotonicityNot monotonic
2024-03-14T20:48:21.123879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 162
 
9.0%
1.0 145
 
8.1%
0.3 144
 
8.0%
0.7 139
 
7.7%
0.4 137
 
7.6%
0.6 131
 
7.3%
0.2 104
 
5.8%
0.8 102
 
5.7%
1.2 64
 
3.6%
2.0 58
 
3.2%
Other values (107) 601
33.4%
ValueCountFrequency (%)
0.0 5
 
0.3%
0.02 3
 
0.2%
0.03 1
 
0.1%
0.04 1
 
0.1%
0.05 2
 
0.1%
0.08 2
 
0.1%
0.1 38
 
2.1%
0.13 1
 
0.1%
0.15 5
 
0.3%
0.2 104
5.8%
ValueCountFrequency (%)
52.0 1
0.1%
47.0 1
0.1%
45.0 1
0.1%
40.9 1
0.1%
35.0 1
0.1%
34.0 1
0.1%
33.0 1
0.1%
28.0 2
0.1%
27.0 2
0.1%
26.0 2
0.1%

총저수량
Real number (ℝ)

HIGH CORRELATION 

Distinct857
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.708571
Minimum0
Maximum400
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:21.400393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8795
Q16.5
median12.95
Q323.7
95-th percentile74.81
Maximum400
Range400
Interquartile range (IQR)17.2

Descriptive statistics

Standard deviation41.457655
Coefficient of variation (CV)1.7486358
Kurtosis30.20119
Mean23.708571
Median Absolute Deviation (MAD)7.47
Skewness5.0464105
Sum42628.01
Variance1718.7371
MonotonicityNot monotonic
2024-03-14T20:48:21.765545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 23
 
1.3%
6.0 20
 
1.1%
17.0 19
 
1.1%
15.0 19
 
1.1%
14.0 18
 
1.0%
4.0 15
 
0.8%
18.0 13
 
0.7%
12.0 13
 
0.7%
11.0 12
 
0.7%
7.8 12
 
0.7%
Other values (847) 1634
90.9%
ValueCountFrequency (%)
0.0 5
0.3%
0.1 1
 
0.1%
0.3 2
 
0.1%
0.31 1
 
0.1%
0.37 1
 
0.1%
0.4 2
 
0.1%
0.5 1
 
0.1%
0.6 7
0.4%
0.67 1
 
0.1%
0.68 1
 
0.1%
ValueCountFrequency (%)
400.0 1
0.1%
357.0 1
0.1%
351.0 1
0.1%
350.0 1
0.1%
345.0 1
0.1%
343.4 1
0.1%
342.4 1
0.1%
342.0 1
0.1%
341.3 1
0.1%
314.8 1
0.1%

사수량
Real number (ℝ)

ZEROS 

Distinct260
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.750634
Minimum-19
Maximum82.9
Zeros486
Zeros (%)27.0%
Negative2
Negative (%)0.1%
Memory size15.9 KiB
2024-03-14T20:48:22.224912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile0
Q10
median0.48
Q31.1675
95-th percentile6.8
Maximum82.9
Range101.9
Interquartile range (IQR)1.1675

Descriptive statistics

Standard deviation5.5372911
Coefficient of variation (CV)3.1630204
Kurtosis76.786646
Mean1.750634
Median Absolute Deviation (MAD)0.48
Skewness7.5894311
Sum3147.64
Variance30.661593
MonotonicityNot monotonic
2024-03-14T20:48:22.484516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 486
27.0%
0.1 92
 
5.1%
1.0 88
 
4.9%
0.2 69
 
3.8%
0.5 65
 
3.6%
2.0 55
 
3.1%
0.3 53
 
2.9%
0.4 44
 
2.4%
0.6 40
 
2.2%
0.9 28
 
1.6%
Other values (250) 778
43.3%
ValueCountFrequency (%)
-19.0 1
 
0.1%
-0.5 1
 
0.1%
0.0 486
27.0%
0.01 1
 
0.1%
0.02 2
 
0.1%
0.03 1
 
0.1%
0.04 1
 
0.1%
0.05 2
 
0.1%
0.08 3
 
0.2%
0.09 5
 
0.3%
ValueCountFrequency (%)
82.9 1
0.1%
78.0 1
0.1%
61.0 1
0.1%
59.0 1
0.1%
57.0 1
0.1%
46.68 1
0.1%
45.0 1
0.1%
41.0 2
0.1%
34.0 1
0.1%
33.3 1
0.1%

유효저수량
Real number (ℝ)

HIGH CORRELATION 

Distinct856
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.957881
Minimum0
Maximum398
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:22.830909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7
Q16.0325
median11.8
Q321.6525
95-th percentile67.4235
Maximum398
Range398
Interquartile range (IQR)15.62

Descriptive statistics

Standard deviation39.687029
Coefficient of variation (CV)1.8074162
Kurtosis32.660133
Mean21.957881
Median Absolute Deviation (MAD)6.8
Skewness5.2425633
Sum39480.27
Variance1575.0603
MonotonicityNot monotonic
2024-03-14T20:48:23.099908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.0 23
 
1.3%
10.0 21
 
1.2%
7.8 17
 
0.9%
6.0 16
 
0.9%
11.0 14
 
0.8%
8.0 13
 
0.7%
3.6 12
 
0.7%
26.0 12
 
0.7%
15.0 12
 
0.7%
10.1 12
 
0.7%
Other values (846) 1646
91.5%
ValueCountFrequency (%)
0.0 5
0.3%
0.1 1
 
0.1%
0.2 1
 
0.1%
0.3 1
 
0.1%
0.31 1
 
0.1%
0.37 1
 
0.1%
0.4 3
0.2%
0.5 6
0.3%
0.6 3
0.2%
0.65 1
 
0.1%
ValueCountFrequency (%)
398.0 1
0.1%
350.0 2
0.1%
342.4 1
0.1%
340.0 2
0.1%
333.7 1
0.1%
331.0 1
0.1%
314.8 1
0.1%
298.4 1
0.1%
282.5 1
0.1%
273.8 1
0.1%

제당형 식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
흙댐
1288 
필댐
509 
석축 토사
 
1

Length

Max length7
Median length2
Mean length2.0027809
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
흙댐 1288
71.6%
필댐 509
 
28.3%
석축 토사 1
 
0.1%

Length

2024-03-14T20:48:23.353123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:48:23.545343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
흙댐 1288
71.6%
필댐 509
 
28.3%
석축 1
 
0.1%
토사 1
 
0.1%

제 당연 장(미터)
Real number (ℝ)

Distinct210
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.189321
Minimum5.4
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:23.757432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.4
5-th percentile39
Q164
median85
Q3113
95-th percentile170
Maximum1200
Range1194.6
Interquartile range (IQR)49

Descriptive statistics

Standard deviation53.668517
Coefficient of variation (CV)0.57590844
Kurtosis131.65121
Mean93.189321
Median Absolute Deviation (MAD)25
Skewness7.6950748
Sum167554.4
Variance2880.3097
MonotonicityNot monotonic
2024-03-14T20:48:24.103802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.0 61
 
3.4%
50.0 59
 
3.3%
60.0 54
 
3.0%
70.0 53
 
2.9%
100.0 49
 
2.7%
90.0 43
 
2.4%
120.0 36
 
2.0%
110.0 36
 
2.0%
85.0 32
 
1.8%
65.0 31
 
1.7%
Other values (200) 1344
74.7%
ValueCountFrequency (%)
5.4 1
 
0.1%
6.0 1
 
0.1%
7.0 1
 
0.1%
12.0 1
 
0.1%
15.0 1
 
0.1%
16.0 1
 
0.1%
19.0 1
 
0.1%
20.0 5
0.3%
22.0 3
0.2%
24.0 1
 
0.1%
ValueCountFrequency (%)
1200.0 1
0.1%
900.0 1
0.1%
516.0 1
0.1%
461.0 1
0.1%
296.0 1
0.1%
290.0 2
0.1%
286.0 1
0.1%
273.0 2
0.1%
268.0 1
0.1%
267.0 1
0.1%

제당높이(미터)
Real number (ℝ)

Distinct152
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2256396
Minimum0.8
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:24.353797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2.5
Q14
median5
Q37.275
95-th percentile13
Maximum115
Range114.2
Interquartile range (IQR)3.275

Descriptive statistics

Standard deviation4.6488699
Coefficient of variation (CV)0.74672968
Kurtosis175.00842
Mean6.2256396
Median Absolute Deviation (MAD)1.5
Skewness8.9879122
Sum11193.7
Variance21.611991
MonotonicityNot monotonic
2024-03-14T20:48:24.611143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 167
 
9.3%
4.0 137
 
7.6%
6.0 118
 
6.6%
3.0 99
 
5.5%
7.0 78
 
4.3%
8.0 71
 
3.9%
10.0 49
 
2.7%
3.5 41
 
2.3%
9.0 36
 
2.0%
4.5 35
 
1.9%
Other values (142) 967
53.8%
ValueCountFrequency (%)
0.8 1
 
0.1%
1.5 7
 
0.4%
1.6 2
 
0.1%
1.7 4
 
0.2%
1.8 4
 
0.2%
2.0 35
1.9%
2.1 1
 
0.1%
2.2 6
 
0.3%
2.3 6
 
0.3%
2.4 7
 
0.4%
ValueCountFrequency (%)
115.0 1
0.1%
49.5 1
0.1%
39.0 1
0.1%
36.0 1
0.1%
35.0 1
0.1%
30.0 1
0.1%
29.5 1
0.1%
26.0 1
0.1%
25.5 1
0.1%
25.0 2
0.1%

제 당정 폭(미터)
Real number (ℝ)

Distinct69
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2968298
Minimum0.5
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:24.867826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.3
Q12
median2.5
Q33
95-th percentile5
Maximum116
Range115.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.3276822
Coefficient of variation (CV)1.9193233
Kurtosis182.97738
Mean3.2968298
Median Absolute Deviation (MAD)0.5
Skewness12.703917
Sum5927.7
Variance40.039561
MonotonicityNot monotonic
2024-03-14T20:48:25.201210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 370
20.6%
2.0 364
20.2%
4.0 165
9.2%
2.5 165
9.2%
1.5 79
 
4.4%
3.5 75
 
4.2%
1.8 56
 
3.1%
1.0 51
 
2.8%
5.0 51
 
2.8%
1.2 30
 
1.7%
Other values (59) 392
21.8%
ValueCountFrequency (%)
0.5 2
 
0.1%
0.6 2
 
0.1%
0.7 1
 
0.1%
1.0 51
2.8%
1.1 2
 
0.1%
1.2 30
 
1.7%
1.3 15
 
0.8%
1.4 25
 
1.4%
1.5 79
4.4%
1.6 28
 
1.6%
ValueCountFrequency (%)
116.0 1
0.1%
105.0 2
0.1%
94.0 1
0.1%
80.0 1
0.1%
74.0 1
0.1%
57.0 1
0.1%
56.0 1
0.1%
54.0 1
0.1%
43.0 1
0.1%
42.0 1
0.1%

설치년도
Real number (ℝ)

Distinct109
Distinct (%)6.1%
Missing5
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1948.57
Minimum1696
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-14T20:48:25.466210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1696
5-th percentile1930
Q11943
median1945
Q31955
95-th percentile1973
Maximum2012
Range316
Interquartile range (IQR)12

Descriptive statistics

Standard deviation17.42467
Coefficient of variation (CV)0.0089422859
Kurtosis33.453746
Mean1948.57
Median Absolute Deviation (MAD)2
Skewness-2.5204412
Sum3493786
Variance303.61912
MonotonicityNot monotonic
2024-03-14T20:48:25.780510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1945 636
35.4%
1943 131
 
7.3%
1944 87
 
4.8%
1968 69
 
3.8%
1942 57
 
3.2%
1940 49
 
2.7%
1969 48
 
2.7%
1967 38
 
2.1%
1970 33
 
1.8%
1948 32
 
1.8%
Other values (99) 613
34.1%
ValueCountFrequency (%)
1696 1
 
0.1%
1800 1
 
0.1%
1823 1
 
0.1%
1838 1
 
0.1%
1842 1
 
0.1%
1845 3
0.2%
1861 2
0.1%
1880 1
 
0.1%
1887 1
 
0.1%
1892 1
 
0.1%
ValueCountFrequency (%)
2012 2
0.1%
2011 1
0.1%
2009 1
0.1%
2008 2
0.1%
2007 1
0.1%
2006 1
0.1%
2005 1
0.1%
2004 1
0.1%
2002 1
0.1%
1999 2
0.1%

Interactions

2024-03-14T20:48:06.384427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:43.517812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:45.660239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:48.381323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:51.031265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:53.822849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:56.213223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:59.028755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:01.742016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:03.886235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:06.623998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:43.783657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:45.928867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:48.643223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:51.306570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:54.088671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:56.494322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:59.294459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:02.009609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:04.085821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:06.797654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:44.051340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:46.198359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:48.908693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:51.581155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:54.561447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:56.771167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:59.568058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:02.281448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:04.359763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:06.961733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:44.315739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:46.461119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:49.164152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:51.848634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:54.828762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:57.054612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:59.829924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:02.542277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:04.622040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:07.143612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:44.500391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:46.748302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:49.440327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:52.132623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:55.012111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:57.359172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:00.113681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:02.782097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:04.907742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:07.374319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:44.662298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:47.009548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:49.695016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:52.400068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:55.195924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:57.623215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:00.373798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:03.029453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:05.168521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:07.637492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:44.846166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:47.292236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:49.972739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:52.698156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:55.371065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:57.887270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:00.660700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:03.211247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:05.456825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:07.885701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:45.014038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:47.560873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:50.238609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:52.972488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:55.550946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:58.173144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:00.925840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:03.377471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:05.677724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:08.053936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:45.183681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:47.843571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:50.502418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:53.249067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:55.714097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:58.475469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:01.196699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:03.545501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:06.032356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:08.227594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:45.386843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:48.110388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:50.765349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:53.533139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:55.945350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:47:58.751534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:01.465707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:03.712468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:48:06.197695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:48:26.065663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별수 혜면 적유 역면 적만 수면 적총저수량사수량유효저수량제당형 식제 당연 장(미터)제당높이(미터)제 당정 폭(미터)설치년도
시군별1.0000.2150.1820.0740.1920.3790.1860.8200.2670.1760.1950.330
수 혜면 적0.2151.0000.4770.5830.6170.5230.6180.2620.7940.3820.0000.267
유 역면 적0.1820.4771.0000.5630.5810.5400.5700.0080.3190.2430.0000.408
만 수면 적0.0740.5830.5631.0000.5560.4670.5340.5610.6660.0590.0000.071
총저수량0.1920.6170.5810.5561.0000.4810.9950.2240.4040.3980.0000.395
사수량0.3790.5230.5400.4670.4811.0000.4570.7040.2650.1160.0000.072
유효저수량0.1860.6180.5700.5340.9950.4571.0000.1280.4240.4460.0000.406
제당형 식0.8200.2620.0080.5610.2240.7040.1281.0000.0000.1650.0000.127
제 당연 장(미터)0.2670.7940.3190.6660.4040.2650.4240.0001.0000.1740.0000.030
제당높이(미터)0.1760.3820.2430.0590.3980.1160.4460.1650.1741.0000.3690.350
제 당정 폭(미터)0.1950.0000.0000.0000.0000.0000.0000.0000.0000.3691.0000.000
설치년도0.3300.2670.4080.0710.3950.0720.4060.1270.0300.3500.0001.000
2024-03-14T20:48:26.346532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별제당형 식
시군별1.0000.675
제당형 식0.6751.000
2024-03-14T20:48:26.536020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수 혜면 적유 역면 적만 수면 적총저수량사수량유효저수량제 당연 장(미터)제당높이(미터)제 당정 폭(미터)설치년도시군별제당형 식
수 혜면 적1.0000.4400.4670.4810.3480.4860.3170.1740.2720.0110.0920.119
유 역면 적0.4401.0000.3320.5140.2290.5030.2230.2560.1710.0910.0740.004
만 수면 적0.4670.3321.0000.5150.2280.5360.4270.0480.168-0.0510.0300.404
총저수량0.4810.5140.5151.0000.3920.9720.3490.2900.1630.0620.0760.130
사수량0.3480.2290.2280.3921.0000.2900.1070.0440.1440.0350.1700.411
유효저수량0.4860.5030.5360.9720.2901.0000.3770.3110.1760.0530.0760.076
제 당연 장(미터)0.3170.2230.4270.3490.1070.3771.000-0.0700.156-0.1130.1020.000
제당높이(미터)0.1740.2560.0480.2900.0440.311-0.0701.0000.1390.2680.0870.069
제 당정 폭(미터)0.2720.1710.1680.1630.1440.1760.1560.1391.0000.1330.0860.000
설치년도0.0110.091-0.0510.0620.0350.053-0.1130.2680.1331.0000.1170.082
시군별0.0920.0740.0300.0760.1700.0760.1020.0870.0860.1171.0000.675
제당형 식0.1190.0040.4040.1300.4110.0760.0000.0690.0000.0820.6751.000

Missing values

2024-03-14T20:48:08.468680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:48:08.911801image/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.
2024-03-14T20:48:09.128989image/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

시군별저수지명읍면리동수 혜면 적유 역면 적만 수면 적총저수량사수량유효저수량제당형 식제 당연 장(미터)제당높이(미터)제 당정 폭(미터)설치년도
0전주시장천제완산평화13.947.23.227.71.0426.66흙댐76.07.03.01964
1전주시작지제완산평화9.837.00.719.50.7518.75흙댐87.06.03.01970
2전주시비아제완산삼천5.636.00.713.60.5213.08흙댐85.06.02.51943
3전주시우묵제완산삼천1.96.20.68.60.338.27흙댐78.05.02.01943
4전주시외절제완산삼천5.910.01.111.990.4611.53흙댐105.06.02.51968
5전주시호동제완산삼천8.925.00.517.120.6616.46흙댐71.06.03.01960
6전주시오리제완산삼천7.110.00.713.90.5313.37흙댐84.06.02.51962
7전주시삼산제완산삼천6.68.00.612.60.4312.17흙댐80.06.02.51943
8전주시능내제완산삼천8.817.09.713.20.512.7흙댐138.06.02.51943
9전주시신덕제완산삼천4.231.00.611.070.4110.66흙댐68.06.02.51943
시군별저수지명읍면리동수 혜면 적유 역면 적만 수면 적총저수량사수량유효저수량제당형 식제 당연 장(미터)제당높이(미터)제 당정 폭(미터)설치년도
1788부안군장신제하서면장신리9.042.01.027.01.026.0필댐65.02.82.01945
1789부안군평지제하서면장신리9.059.01.027.01.026.0필댐120.04.33.01945
1790부안군하서제하서면장신리4.020.01.012.02.010.0필댐80.06.41.01945
1791부안군감동제줄포면우포리3.515.01.07.00.07.0필댐48.03.32.01945
1792부안군목상제줄포면난산리5.020.02.040.01.039.0필댐118.03.72.01945
1793부안군반월제줄포면파산리5.05.01.010.00.010.0필댐75.02.62.01945
1794부안군사거제줄포면장동리3.020.02.017.01.016.0필댐91.02.33.51945
1795부안군선양제줄포면우포리12.015.02.020.01.019.0필댐194.05.02.01945
1796부안군은향제줄포면줄포리33.07.01.010.00.010.0필댐86.03.42.01945
1797부안군옹암제줄포면우포리3.54.00.52.40.02.4필댐48.03.32.01945