Overview

Dataset statistics

Number of variables11
Number of observations119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 KiB
Average record size in memory96.1 B

Variable types

Numeric7
Text2
Categorical2

Dataset

Description경상남도 남해군의 저수지현황 입니다. 저수지명, 소재지, 유역면적, 유효저수용량, 댐높이, 댐길이, 순관개면적, 준공년도, 관할기관명, 관리기관연락처 등 정보를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15039824/fileData.do

Alerts

유역면적(헥타르) is highly overall correlated with 관할기관명 and 1 other fieldsHigh correlation
유효저수용량(세제곱미터) is highly overall correlated with 댐높이(미터) and 3 other fieldsHigh correlation
댐높이(미터) is highly overall correlated with 유효저수용량(세제곱미터)High correlation
순관개면적(헥타르)몽리면적 is highly overall correlated with 유효저수용량(세제곱미터)High correlation
관할기관명 is highly overall correlated with 유역면적(헥타르) and 2 other fieldsHigh correlation
관리기관연락처 is highly overall correlated with 유역면적(헥타르) and 2 other fieldsHigh correlation
관할기관명 is highly imbalanced (83.0%)Imbalance
관리기관연락처 is highly imbalanced (83.0%)Imbalance
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:10:23.590315
Analysis finished2023-12-12 05:10:29.855034
Duration6.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:10:29.927937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.9
Q130.5
median60
Q389.5
95-th percentile113.1
Maximum119
Range118
Interquartile range (IQR)59

Descriptive statistics

Standard deviation34.496377
Coefficient of variation (CV)0.57493961
Kurtosis-1.2
Mean60
Median Absolute Deviation (MAD)30
Skewness0
Sum7140
Variance1190
MonotonicityStrictly increasing
2023-12-12T14:10:30.120218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
2 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
82 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%
111 1
0.8%
110 1
0.8%

저수지명
Text

Distinct118
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T14:10:30.539569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.9411765
Min length2

Characters and Unicode

Total characters350
Distinct characters114
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

Unique117 ?
Unique (%)98.3%

Sample

1st row상심천
2nd row모 산
3rd row신 촌
4th row대입현1
5th row대입현2
ValueCountFrequency (%)
금 4
 
2.9%
송 3
 
2.1%
촌 3
 
2.1%
지족 2
 
1.4%
양 2
 
1.4%
천 2
 
1.4%
산 2
 
1.4%
지 2
 
1.4%
동비구 1
 
0.7%
동비신 1
 
0.7%
Other values (118) 118
84.3%
2023-12-12T14:10:31.054236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63
 
18.0%
신 13
 
3.7%
구 11
 
3.1%
천 9
 
2.6%
동 9
 
2.6%
1 8
 
2.3%
곡 7
 
2.0%
금 7
 
2.0%
산 7
 
2.0%
현 7
 
2.0%
Other values (104) 209
59.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 271
77.4%
Space Separator 63
 
18.0%
Decimal Number 16
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 13
 
4.8%
구 11
 
4.1%
천 9
 
3.3%
동 9
 
3.3%
곡 7
 
2.6%
금 7
 
2.6%
산 7
 
2.6%
현 7
 
2.6%
상 6
 
2.2%
항 6
 
2.2%
Other values (100) 189
69.7%
Decimal Number
ValueCountFrequency (%)
1 8
50.0%
2 7
43.8%
3 1
 
6.2%
Space Separator
ValueCountFrequency (%)
63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 271
77.4%
Common 79
 
22.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 13
 
4.8%
구 11
 
4.1%
천 9
 
3.3%
동 9
 
3.3%
곡 7
 
2.6%
금 7
 
2.6%
산 7
 
2.6%
현 7
 
2.6%
상 6
 
2.2%
항 6
 
2.2%
Other values (100) 189
69.7%
Common
ValueCountFrequency (%)
63
79.7%
1 8
 
10.1%
2 7
 
8.9%
3 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 271
77.4%
ASCII 79
 
22.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63
79.7%
1 8
 
10.1%
2 7
 
8.9%
3 1
 
1.3%
Hangul
ValueCountFrequency (%)
신 13
 
4.8%
구 11
 
4.1%
천 9
 
3.3%
동 9
 
3.3%
곡 7
 
2.6%
금 7
 
2.6%
산 7
 
2.6%
현 7
 
2.6%
상 6
 
2.2%
항 6
 
2.2%
Other values (100) 189
69.7%

소재지
Text

Distinct118
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T14:10:31.318030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length20.092437
Min length17

Characters and Unicode

Total characters2391
Distinct characters93
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)98.3%

Sample

1st row경상남도 남해군 남해읍 심천리1243,1225
2nd row경상남도 남해군 남해읍 심천리550-2
3rd row경상남도 남해군 남해읍 평현리527-1
4th row경상남도 남해군 남해읍 입현리1175
5th row경상남도 남해군 남해읍 입현리1051
ValueCountFrequency (%)
경상남도 119
24.9%
남해군 119
24.9%
남면 22
 
4.6%
설천면 21
 
4.4%
창선면 17
 
3.6%
고현면 14
 
2.9%
서면 14
 
2.9%
남해읍 10
 
2.1%
삼동면 10
 
2.1%
이동면 5
 
1.0%
Other values (121) 126
26.4%
2023-12-12T14:10:31.707065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
358
15.0%
남 276
 
11.5%
상 135
 
5.6%
해 129
 
5.4%
도 123
 
5.1%
리 121
 
5.1%
경 119
 
5.0%
군 119
 
5.0%
면 111
 
4.6%
1 97
 
4.1%
Other values (83) 803
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1520
63.6%
Decimal Number 452
 
18.9%
Space Separator 358
 
15.0%
Dash Punctuation 55
 
2.3%
Other Punctuation 4
 
0.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 276
18.2%
상 135
8.9%
해 129
 
8.5%
도 123
 
8.1%
리 121
 
8.0%
경 119
 
7.8%
군 119
 
7.8%
면 111
 
7.3%
현 27
 
1.8%
천 26
 
1.7%
Other values (68) 334
22.0%
Decimal Number
ValueCountFrequency (%)
1 97
21.5%
2 72
15.9%
3 49
10.8%
8 39
8.6%
7 38
 
8.4%
6 33
 
7.3%
5 33
 
7.3%
4 32
 
7.1%
0 31
 
6.9%
9 28
 
6.2%
Space Separator
ValueCountFrequency (%)
358
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1520
63.6%
Common 871
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 276
18.2%
상 135
8.9%
해 129
 
8.5%
도 123
 
8.1%
리 121
 
8.0%
경 119
 
7.8%
군 119
 
7.8%
면 111
 
7.3%
현 27
 
1.8%
천 26
 
1.7%
Other values (68) 334
22.0%
Common
ValueCountFrequency (%)
358
41.1%
1 97
 
11.1%
2 72
 
8.3%
- 55
 
6.3%
3 49
 
5.6%
8 39
 
4.5%
7 38
 
4.4%
6 33
 
3.8%
5 33
 
3.8%
4 32
 
3.7%
Other values (5) 65
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1520
63.6%
ASCII 871
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
358
41.1%
1 97
 
11.1%
2 72
 
8.3%
- 55
 
6.3%
3 49
 
5.6%
8 39
 
4.5%
7 38
 
4.4%
6 33
 
3.8%
5 33
 
3.8%
4 32
 
3.7%
Other values (5) 65
 
7.5%
Hangul
ValueCountFrequency (%)
남 276
18.2%
상 135
8.9%
해 129
 
8.5%
도 123
 
8.1%
리 121
 
8.0%
경 119
 
7.8%
군 119
 
7.8%
면 111
 
7.3%
현 27
 
1.8%
천 26
 
1.7%
Other values (68) 334
22.0%

유역면적(헥타르)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.904202
Minimum3
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:10:31.858632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.9
Q116
median31
Q360
95-th percentile101
Maximum270
Range267
Interquartile range (IQR)44

Descriptive statistics

Standard deviation38.028721
Coefficient of variation (CV)0.92970207
Kurtosis11.920745
Mean40.904202
Median Absolute Deviation (MAD)16
Skewness2.7679378
Sum4867.6
Variance1446.1836
MonotonicityNot monotonic
2023-12-12T14:10:32.012202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 7
 
5.9%
20.0 5
 
4.2%
45.0 5
 
4.2%
35.0 5
 
4.2%
28.0 4
 
3.4%
22.0 4
 
3.4%
62.0 4
 
3.4%
21.0 3
 
2.5%
11.0 3
 
2.5%
12.0 3
 
2.5%
Other values (50) 76
63.9%
ValueCountFrequency (%)
3.0 2
1.7%
4.0 2
1.7%
5.0 2
1.7%
6.0 2
1.7%
7.0 2
1.7%
8.0 1
 
0.8%
9.0 1
 
0.8%
10.0 1
 
0.8%
11.0 3
2.5%
12.0 3
2.5%
ValueCountFrequency (%)
270.0 1
0.8%
177.0 1
0.8%
167.0 1
0.8%
116.0 1
0.8%
110.0 2
1.7%
100.0 1
0.8%
97.0 2
1.7%
95.0 1
0.8%
91.0 1
0.8%
76.0 2
1.7%

유효저수용량(세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.713697
Minimum1.6
Maximum369.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:10:32.227843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile2
Q16.345
median9.23
Q315.715
95-th percentile71.4
Maximum369.72
Range368.12
Interquartile range (IQR)9.37

Descriptive statistics

Standard deviation45.792121
Coefficient of variation (CV)2.1089048
Kurtosis32.557106
Mean21.713697
Median Absolute Deviation (MAD)3.87
Skewness5.2709515
Sum2583.93
Variance2096.9184
MonotonicityNot monotonic
2023-12-12T14:10:32.414876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 3
 
2.5%
7.6 3
 
2.5%
16.2 3
 
2.5%
9.0 3
 
2.5%
10.0 2
 
1.7%
14.85 2
 
1.7%
12.0 2
 
1.7%
10.5 2
 
1.7%
2.0 2
 
1.7%
30.0 2
 
1.7%
Other values (93) 95
79.8%
ValueCountFrequency (%)
1.6 1
0.8%
1.64 1
0.8%
1.7 1
0.8%
1.88 1
0.8%
1.95 1
0.8%
2.0 2
1.7%
2.63 1
0.8%
3.0 1
0.8%
3.05 1
0.8%
3.3 1
0.8%
ValueCountFrequency (%)
369.72 1
0.8%
222.0 1
0.8%
195.0 1
0.8%
150.2 1
0.8%
112.2 1
0.8%
102.0 1
0.8%
68.0 1
0.8%
62.2 1
0.8%
49.2 1
0.8%
45.6 1
0.8%

댐높이(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8638655
Minimum3
Maximum18.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:10:32.597637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median7
Q39.95
95-th percentile15
Maximum18.2
Range15.2
Interquartile range (IQR)4.95

Descriptive statistics

Standard deviation3.5768022
Coefficient of variation (CV)0.4548402
Kurtosis0.55770699
Mean7.8638655
Median Absolute Deviation (MAD)2
Skewness1.0651136
Sum935.8
Variance12.793514
MonotonicityNot monotonic
2023-12-12T14:10:32.796731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5.0 24
20.2%
9.0 14
11.8%
6.0 11
9.2%
4.0 10
8.4%
7.0 9
 
7.6%
10.0 8
 
6.7%
8.0 7
 
5.9%
13.0 5
 
4.2%
5.5 4
 
3.4%
15.0 4
 
3.4%
Other values (15) 23
19.3%
ValueCountFrequency (%)
3.0 3
 
2.5%
4.0 10
8.4%
4.5 2
 
1.7%
5.0 24
20.2%
5.5 4
 
3.4%
6.0 11
9.2%
6.3 1
 
0.8%
6.5 1
 
0.8%
6.8 1
 
0.8%
7.0 9
 
7.6%
ValueCountFrequency (%)
18.2 1
 
0.8%
18.0 1
 
0.8%
17.4 2
 
1.7%
16.3 1
 
0.8%
15.0 4
3.4%
14.0 1
 
0.8%
13.0 5
4.2%
12.5 1
 
0.8%
12.0 3
2.5%
11.0 3
2.5%

댐길이(미터)
Real number (ℝ)

Distinct67
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.156303
Minimum11.6
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:10:33.026347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile30.9
Q147.5
median70
Q3110
95-th percentile168.7
Maximum254
Range242.4
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation45.079802
Coefficient of variation (CV)0.54870778
Kurtosis1.3135434
Mean82.156303
Median Absolute Deviation (MAD)26
Skewness1.153672
Sum9776.6
Variance2032.1886
MonotonicityNot monotonic
2023-12-12T14:10:33.243004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.0 7
 
5.9%
70.0 6
 
5.0%
45.0 6
 
5.0%
60.0 5
 
4.2%
50.0 5
 
4.2%
40.0 5
 
4.2%
110.0 4
 
3.4%
90.0 4
 
3.4%
120.0 4
 
3.4%
51.0 3
 
2.5%
Other values (57) 70
58.8%
ValueCountFrequency (%)
11.6 1
 
0.8%
29.0 2
 
1.7%
30.0 3
2.5%
31.0 1
 
0.8%
33.0 1
 
0.8%
35.0 1
 
0.8%
36.0 2
 
1.7%
37.0 1
 
0.8%
38.0 1
 
0.8%
40.0 5
4.2%
ValueCountFrequency (%)
254.0 1
 
0.8%
210.0 1
 
0.8%
206.0 1
 
0.8%
183.0 1
 
0.8%
175.0 2
1.7%
168.0 1
 
0.8%
160.0 2
1.7%
149.0 1
 
0.8%
142.0 1
 
0.8%
140.0 3
2.5%

순관개면적(헥타르)몽리면적
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5672269
Minimum1.5
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:10:33.435210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.89
Q14.65
median6.4
Q310
95-th percentile22.1
Maximum38
Range36.5
Interquartile range (IQR)5.35

Descriptive statistics

Standard deviation6.4260475
Coefficient of variation (CV)0.75007322
Kurtosis6.017622
Mean8.5672269
Median Absolute Deviation (MAD)2.5
Skewness2.2657219
Sum1019.5
Variance41.294086
MonotonicityNot monotonic
2023-12-12T14:10:33.629588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 13
 
10.9%
10.0 10
 
8.4%
8.0 8
 
6.7%
4.0 6
 
5.0%
6.0 6
 
5.0%
3.0 6
 
5.0%
15.0 3
 
2.5%
6.4 3
 
2.5%
11.0 3
 
2.5%
4.3 2
 
1.7%
Other values (48) 59
49.6%
ValueCountFrequency (%)
1.5 1
 
0.8%
1.9 1
 
0.8%
2.5 1
 
0.8%
2.7 1
 
0.8%
2.8 2
 
1.7%
2.9 1
 
0.8%
3.0 6
5.0%
3.3 1
 
0.8%
3.6 1
 
0.8%
3.7 2
 
1.7%
ValueCountFrequency (%)
38.0 1
 
0.8%
31.4 1
 
0.8%
30.0 2
1.7%
28.0 1
 
0.8%
23.0 1
 
0.8%
22.0 1
 
0.8%
21.8 1
 
0.8%
17.0 1
 
0.8%
15.5 1
 
0.8%
15.0 3
2.5%

준공년도
Real number (ℝ)

Distinct25
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1962.6387
Minimum1945
Maximum2008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:10:33.774994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1945
Q11945
median1967
Q31968
95-th percentile1984.2
Maximum2008
Range63
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.291595
Coefficient of variation (CV)0.0072818268
Kurtosis0.39996896
Mean1962.6387
Median Absolute Deviation (MAD)5
Skewness0.40577482
Sum233554
Variance204.24968
MonotonicityNot monotonic
2023-12-12T14:10:33.949630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1945 38
31.9%
1968 30
25.2%
1967 16
13.4%
1972 5
 
4.2%
1966 4
 
3.4%
1965 3
 
2.5%
1969 2
 
1.7%
1983 2
 
1.7%
1971 2
 
1.7%
1975 2
 
1.7%
Other values (15) 15
 
12.6%
ValueCountFrequency (%)
1945 38
31.9%
1948 1
 
0.8%
1959 1
 
0.8%
1962 1
 
0.8%
1965 3
 
2.5%
1966 4
 
3.4%
1967 16
13.4%
1968 30
25.2%
1969 2
 
1.7%
1971 2
 
1.7%
ValueCountFrequency (%)
2008 1
0.8%
2007 1
0.8%
1996 1
0.8%
1995 1
0.8%
1994 1
0.8%
1986 1
0.8%
1984 1
0.8%
1983 2
1.7%
1978 1
0.8%
1977 1
0.8%

관할기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
남해군 건설교통과
116 
농어촌공사 남해지사
 
3

Length

Max length10
Median length9
Mean length9.0252101
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남해군 건설교통과
2nd row남해군 건설교통과
3rd row남해군 건설교통과
4th row남해군 건설교통과
5th row남해군 건설교통과

Common Values

ValueCountFrequency (%)
남해군 건설교통과 116
97.5%
농어촌공사 남해지사 3
 
2.5%

Length

2023-12-12T14:10:34.152978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:34.271775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남해군 116
48.7%
건설교통과 116
48.7%
농어촌공사 3
 
1.3%
남해지사 3
 
1.3%

관리기관연락처
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
055-860-3303
116 
055-864-3724
 
3

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row055-860-3303
2nd row055-860-3303
3rd row055-860-3303
4th row055-860-3303
5th row055-860-3303

Common Values

ValueCountFrequency (%)
055-860-3303 116
97.5%
055-864-3724 3
 
2.5%

Length

2023-12-12T14:10:34.408330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:34.846966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
055-860-3303 116
97.5%
055-864-3724 3
 
2.5%

Interactions

2023-12-12T14:10:28.924724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.024951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.783120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.554594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.436603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.264712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.215734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:29.058349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.117131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.870461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.691687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.575315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.371349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.302635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:29.148354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.249092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.956064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.839731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.701109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.461279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.393481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:29.251755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.353794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.090522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.954477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.826825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.810309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.485908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:29.347787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.447526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.202583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.065281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.926276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.893591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.600521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:29.422060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.573052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.333765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.175536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.025854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.980411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.697579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:29.507762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:24.691519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:25.449461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:26.281862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:27.143722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.125098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:28.817048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:10:34.944534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호유역면적(헥타르)유효저수용량(세제곱미터)댐높이(미터)댐길이(미터)순관개면적(헥타르)몽리면적준공년도관할기관명관리기관연락처
번호1.0000.0000.0000.0000.2110.2480.0000.0000.000
유역면적(헥타르)0.0001.0000.8790.2160.3880.4530.0000.5570.557
유효저수용량(세제곱미터)0.0000.8791.0000.6590.7550.7880.6910.5050.505
댐높이(미터)0.0000.2160.6591.0000.6200.5510.5780.1720.172
댐길이(미터)0.2110.3880.7550.6201.0000.7000.7710.0000.000
순관개면적(헥타르)몽리면적0.2480.4530.7880.5510.7001.0000.5850.0000.000
준공년도0.0000.0000.6910.5780.7710.5851.0000.0000.000
관할기관명0.0000.5570.5050.1720.0000.0000.0001.0000.963
관리기관연락처0.0000.5570.5050.1720.0000.0000.0000.9631.000
2023-12-12T14:10:35.080591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관연락처관할기관명
관리기관연락처1.0000.827
관할기관명0.8271.000
2023-12-12T14:10:35.185903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호유역면적(헥타르)유효저수용량(세제곱미터)댐높이(미터)댐길이(미터)순관개면적(헥타르)몽리면적준공년도관할기관명관리기관연락처
번호1.000-0.037-0.0400.1470.1100.0740.2130.0000.000
유역면적(헥타르)-0.0371.0000.2800.1360.0590.2690.0620.5850.585
유효저수용량(세제곱미터)-0.0400.2801.0000.5390.1890.5980.1330.5300.530
댐높이(미터)0.1470.1360.5391.000-0.1490.4080.3550.1240.124
댐길이(미터)0.1100.0590.189-0.1491.0000.186-0.2190.0000.000
순관개면적(헥타르)몽리면적0.0740.2690.5980.4080.1861.0000.1470.0000.000
준공년도0.2130.0620.1330.355-0.2190.1471.0000.0000.000
관할기관명0.0000.5850.5300.1240.0000.0000.0001.0000.827
관리기관연락처0.0000.5850.5300.1240.0000.0000.0000.8271.000

Missing values

2023-12-12T14:10:29.627908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:10:29.788567image/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

번호저수지명소재지유역면적(헥타르)유효저수용량(세제곱미터)댐높이(미터)댐길이(미터)순관개면적(헥타르)몽리면적준공년도관할기관명관리기관연락처
01상심천경상남도 남해군 남해읍 심천리1243,122566.043.712.560.010.01959남해군 건설교통과055-860-3303
12모 산경상남도 남해군 남해읍 심천리550-223.04.163.0175.09.81945남해군 건설교통과055-860-3303
23신 촌경상남도 남해군 남해읍 평현리527-135.07.225.5140.05.71945남해군 건설교통과055-860-3303
34대입현1경상남도 남해군 남해읍 입현리117531.019.06.395.05.01945남해군 건설교통과055-860-3303
45대입현2경상남도 남해군 남해읍 입현리10517.015.5811.080.05.01972남해군 건설교통과055-860-3303
56토 촌경상남도 남해군 남해읍 입현리77228.016.24.5127.011.01945남해군 건설교통과055-860-3303
67섬 호경상남도 남해군 남해읍 입현리1406.07.446.080.04.91945남해군 건설교통과055-860-3303
78외 금경상남도 남해군 남해읍 평리리467-176.014.25.031.09.01968남해군 건설교통과055-860-3303
89내 금경상남도 남해군 남해읍 평리리5216.026.49.072.011.01972남해군 건설교통과055-860-3303
910아 산경상남도 남해군 남해읍 아산리90-1,228270.0369.726.8110.010.01974농어촌공사 남해지사055-864-3724
번호저수지명소재지유역면적(헥타르)유효저수용량(세제곱미터)댐높이(미터)댐길이(미터)순관개면적(헥타르)몽리면적준공년도관할기관명관리기관연락처
109110지족경상남도 남해군 창선면 지족리2168.07.028.040.03.01968남해군 건설교통과055-860-3303
110111연곡경상남도 남해군 창선면 오용리112762.030.07.0140.06.41968남해군 건설교통과055-860-3303
111112가인경상남도 남해군 창선면 가인리산207-722.012.89.070.04.11968남해군 건설교통과055-860-3303
112113단항1경상남도 남해군 창선면 대벽리431-115.06.056.0120.04.51968남해군 건설교통과055-860-3303
113114단항2경상남도 남해군 창선면 대벽리33828.02.05.0120.03.01945남해군 건설교통과055-860-3303
114115대곡2경상남도 남해군 창선면 진동리739-263.08.255.080.015.51976남해군 건설교통과055-860-3303
115116청산경상남도 남해군 창선면 지족리13-222.08.76.060.05.51968남해군 건설교통과055-860-3303
116117광천경상남도 남해군 창선면 광천리886-497.06.954.070.07.51945남해군 건설교통과055-860-3303
117118독망골경상남도 남해군 창선면 부윤리산23-240.0195.016.3124.030.02007남해군 건설교통과055-860-3303
118119염해경상남도 남해군 서면 남상리 53923.08.849.911.68.01994남해군 건설교통과055-860-3303