Overview

Dataset statistics

Number of variables11
Number of observations121
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 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.
Author경상남도 남해군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15039824

Alerts

관할기관명 has constant value ""Constant
관리기관연락처 has constant value ""Constant
유효저수용량( ㎥) is highly overall correlated with 댐높이(m) and 1 other fieldsHigh correlation
댐높이(m) is highly overall correlated with 유효저수용량( ㎥)High correlation
순관개면적(㏊)몽리면적 is highly overall correlated with 유효저수용량( ㎥)High correlation
번호 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:44:57.627901
Analysis finished2023-12-10 23:45:03.298553
Duration5.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:45:03.395334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q131
median61
Q391
95-th percentile115
Maximum121
Range120
Interquartile range (IQR)60

Descriptive statistics

Standard deviation35.073732
Coefficient of variation (CV)0.57497921
Kurtosis-1.2
Mean61
Median Absolute Deviation (MAD)30
Skewness0
Sum7381
Variance1230.1667
MonotonicityStrictly increasing
2023-12-11T08:45:03.560871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
92 1
 
0.8%
90 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%
Other values (111) 111
91.7%
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 (%)
121 1
0.8%
120 1
0.8%
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%

저수지명
Text

Distinct119
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T08:45:03.904751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.446281
Min length2

Characters and Unicode

Total characters296
Distinct characters115
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 (%)96.7%

Sample

1st row상심천
2nd row모산
3rd row신촌
4th row대입현1
5th row대입현2
ValueCountFrequency (%)
지족 2
 
1.6%
양지 2
 
1.6%
관당신 1
 
0.8%
금음신 1
 
0.8%
금음구 1
 
0.8%
고사 1
 
0.8%
동비신 1
 
0.8%
동비구 1
 
0.8%
정태 1
 
0.8%
남양 1
 
0.8%
Other values (110) 110
90.2%
2023-12-11T08:45:04.488685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 13
 
4.4%
구 12
 
4.1%
천 9
 
3.0%
동 9
 
3.0%
1 9
 
3.0%
금 8
 
2.7%
2 8
 
2.7%
산 7
 
2.4%
곡 7
 
2.4%
상 7
 
2.4%
Other values (105) 207
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 276
93.2%
Decimal Number 18
 
6.1%
Space Separator 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 13
 
4.7%
구 12
 
4.3%
천 9
 
3.3%
동 9
 
3.3%
금 8
 
2.9%
산 7
 
2.5%
곡 7
 
2.5%
상 7
 
2.5%
현 7
 
2.5%
대 6
 
2.2%
Other values (101) 191
69.2%
Decimal Number
ValueCountFrequency (%)
1 9
50.0%
2 8
44.4%
3 1
 
5.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 276
93.2%
Common 20
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 13
 
4.7%
구 12
 
4.3%
천 9
 
3.3%
동 9
 
3.3%
금 8
 
2.9%
산 7
 
2.5%
곡 7
 
2.5%
상 7
 
2.5%
현 7
 
2.5%
대 6
 
2.2%
Other values (101) 191
69.2%
Common
ValueCountFrequency (%)
1 9
45.0%
2 8
40.0%
2
 
10.0%
3 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 276
93.2%
ASCII 20
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 13
 
4.7%
구 12
 
4.3%
천 9
 
3.3%
동 9
 
3.3%
금 8
 
2.9%
산 7
 
2.5%
곡 7
 
2.5%
상 7
 
2.5%
현 7
 
2.5%
대 6
 
2.2%
Other values (101) 191
69.2%
ASCII
ValueCountFrequency (%)
1 9
45.0%
2 8
40.0%
2
 
10.0%
3 1
 
5.0%

소재지
Text

UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T08:45:04.826267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length20.07438
Min length17

Characters and Unicode

Total characters2429
Distinct characters96
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

Unique121 ?
Unique (%)100.0%

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 121
25.0%
남해군 121
25.0%
남면 22
 
4.5%
설천면 21
 
4.3%
창선면 17
 
3.5%
고현면 15
 
3.1%
서면 14
 
2.9%
남해읍 11
 
2.3%
삼동면 10
 
2.1%
이동면 5
 
1.0%
Other values (123) 127
26.2%
2023-12-11T08:45:05.269277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
363
14.9%
남 280
 
11.5%
상 136
 
5.6%
해 132
 
5.4%
도 125
 
5.1%
경 121
 
5.0%
리 121
 
5.0%
군 121
 
5.0%
면 112
 
4.6%
1 98
 
4.0%
Other values (86) 820
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1543
63.5%
Decimal Number 461
 
19.0%
Space Separator 363
 
14.9%
Dash Punctuation 56
 
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 280
18.1%
상 136
 
8.8%
해 132
 
8.6%
도 125
 
8.1%
경 121
 
7.8%
리 121
 
7.8%
군 121
 
7.8%
면 112
 
7.3%
현 28
 
1.8%
천 26
 
1.7%
Other values (71) 341
22.1%
Decimal Number
ValueCountFrequency (%)
1 98
21.3%
2 74
16.1%
3 49
10.6%
8 41
8.9%
7 40
8.7%
4 34
 
7.4%
5 34
 
7.4%
6 33
 
7.2%
0 31
 
6.7%
9 27
 
5.9%
Space Separator
ValueCountFrequency (%)
363
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
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 1543
63.5%
Common 886
36.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 280
18.1%
상 136
 
8.8%
해 132
 
8.6%
도 125
 
8.1%
경 121
 
7.8%
리 121
 
7.8%
군 121
 
7.8%
면 112
 
7.3%
현 28
 
1.8%
천 26
 
1.7%
Other values (71) 341
22.1%
Common
ValueCountFrequency (%)
363
41.0%
1 98
 
11.1%
2 74
 
8.4%
- 56
 
6.3%
3 49
 
5.5%
8 41
 
4.6%
7 40
 
4.5%
4 34
 
3.8%
5 34
 
3.8%
6 33
 
3.7%
Other values (5) 64
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1543
63.5%
ASCII 886
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
363
41.0%
1 98
 
11.1%
2 74
 
8.4%
- 56
 
6.3%
3 49
 
5.5%
8 41
 
4.6%
7 40
 
4.5%
4 34
 
3.8%
5 34
 
3.8%
6 33
 
3.7%
Other values (5) 64
 
7.2%
Hangul
ValueCountFrequency (%)
남 280
18.1%
상 136
 
8.8%
해 132
 
8.6%
도 125
 
8.1%
경 121
 
7.8%
리 121
 
7.8%
군 121
 
7.8%
면 112
 
7.3%
현 28
 
1.8%
천 26
 
1.7%
Other values (71) 341
22.1%

유역면적(㏊)
Real number (ℝ)

Distinct60
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.550413
Minimum3
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:45:05.428228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q116
median31
Q360
95-th percentile97
Maximum177
Range174
Interquartile range (IQR)44

Descriptive statistics

Standard deviation31.497685
Coefficient of variation (CV)0.79639333
Kurtosis4.0112336
Mean39.550413
Median Absolute Deviation (MAD)16
Skewness1.6772632
Sum4785.6
Variance992.10419
MonotonicityNot monotonic
2023-12-11T08:45:05.598545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 6
 
5.0%
45.0 5
 
4.1%
35.0 5
 
4.1%
20.0 5
 
4.1%
62.0 4
 
3.3%
22.0 4
 
3.3%
28.0 4
 
3.3%
21.0 3
 
2.5%
13.0 3
 
2.5%
12.0 3
 
2.5%
Other values (50) 79
65.3%
ValueCountFrequency (%)
3.0 2
1.7%
4.0 1
 
0.8%
5.0 2
1.7%
6.0 2
1.7%
7.0 3
2.5%
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 (%)
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 3
2.5%
70.0 1
 
0.8%

유효저수용량( ㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.781322
Minimum1.02
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:45:05.787533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile2
Q16.32
median9.43
Q315.6
95-th percentile64
Maximum222
Range220.98
Interquartile range (IQR)9.28

Descriptive statistics

Standard deviation32.571546
Coefficient of variation (CV)1.734252
Kurtosis21.133733
Mean18.781322
Median Absolute Deviation (MAD)3.95
Skewness4.3544625
Sum2272.54
Variance1060.9056
MonotonicityNot monotonic
2023-12-11T08:45:05.928611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.0 3
 
2.5%
16.2 3
 
2.5%
7.6 3
 
2.5%
8.0 3
 
2.5%
10.0 2
 
1.7%
8.8 2
 
1.7%
30.0 2
 
1.7%
10.5 2
 
1.7%
14.85 2
 
1.7%
12.0 2
 
1.7%
Other values (93) 97
80.2%
ValueCountFrequency (%)
1.02 1
0.8%
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%
ValueCountFrequency (%)
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%
64.0 1
0.8%
62.2 1
0.8%
49.2 1
0.8%
45.6 1
0.8%

댐높이(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7578512
Minimum3
Maximum18.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:45:06.099016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median7
Q39
95-th percentile15
Maximum18.2
Range15.2
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5634195
Coefficient of variation (CV)0.45933074
Kurtosis0.69922307
Mean7.7578512
Median Absolute Deviation (MAD)2
Skewness1.1090468
Sum938.7
Variance12.697959
MonotonicityNot monotonic
2023-12-11T08:45:06.237658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5.0 24
19.8%
9.0 14
11.6%
6.0 11
9.1%
4.0 10
 
8.3%
7.0 9
 
7.4%
10.0 8
 
6.6%
8.0 7
 
5.8%
13.0 5
 
4.1%
5.5 4
 
3.3%
3.0 4
 
3.3%
Other values (17) 25
20.7%
ValueCountFrequency (%)
3.0 4
 
3.3%
4.0 10
8.3%
4.5 2
 
1.7%
4.8 1
 
0.8%
5.0 24
19.8%
5.5 4
 
3.3%
6.0 11
9.1%
6.3 1
 
0.8%
6.5 1
 
0.8%
6.8 1
 
0.8%
ValueCountFrequency (%)
18.2 1
 
0.8%
18.0 1
 
0.8%
17.4 2
 
1.7%
16.3 1
 
0.8%
15.0 4
3.3%
14.0 1
 
0.8%
13.0 5
4.1%
12.5 1
 
0.8%
12.0 2
 
1.7%
11.0 3
2.5%

댐길이(m)
Real number (ℝ)

Distinct67
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.586777
Minimum26
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:45:06.415900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile31
Q149
median70
Q3115
95-th percentile168
Maximum254
Range228
Interquartile range (IQR)66

Descriptive statistics

Standard deviation45.050096
Coefficient of variation (CV)0.538962
Kurtosis1.0992043
Mean83.586777
Median Absolute Deviation (MAD)29
Skewness1.0854785
Sum10114
Variance2029.5112
MonotonicityNot monotonic
2023-12-11T08:45:06.543853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 7
 
5.8%
70 6
 
5.0%
45 6
 
5.0%
60 5
 
4.1%
50 5
 
4.1%
40 5
 
4.1%
120 5
 
4.1%
90 4
 
3.3%
110 4
 
3.3%
140 3
 
2.5%
Other values (57) 71
58.7%
ValueCountFrequency (%)
26 1
 
0.8%
29 2
 
1.7%
30 2
 
1.7%
31 2
 
1.7%
33 1
 
0.8%
35 1
 
0.8%
36 2
 
1.7%
37 1
 
0.8%
38 1
 
0.8%
40 5
4.1%
ValueCountFrequency (%)
254 1
 
0.8%
210 1
 
0.8%
206 1
 
0.8%
183 1
 
0.8%
175 2
1.7%
168 1
 
0.8%
160 2
1.7%
149 1
 
0.8%
142 1
 
0.8%
140 3
2.5%

순관개면적(㏊)몽리면적
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4876033
Minimum1.5
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:45:06.698348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.8
Q14.6
median6.4
Q310
95-th percentile22
Maximum38
Range36.5
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation6.4105846
Coefficient of variation (CV)0.75528796
Kurtosis6.0725459
Mean8.4876033
Median Absolute Deviation (MAD)2.6
Skewness2.2702565
Sum1027
Variance41.095595
MonotonicityNot monotonic
2023-12-11T08:45:06.880426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 14
 
11.6%
10.0 10
 
8.3%
3.0 7
 
5.8%
8.0 7
 
5.8%
6.0 6
 
5.0%
4.0 5
 
4.1%
11.0 3
 
2.5%
9.0 3
 
2.5%
6.4 3
 
2.5%
15.0 3
 
2.5%
Other values (48) 60
49.6%
ValueCountFrequency (%)
1.5 1
 
0.8%
1.9 1
 
0.8%
2.5 2
 
1.7%
2.7 1
 
0.8%
2.8 2
 
1.7%
2.9 1
 
0.8%
3.0 7
5.8%
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)

Distinct24
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1962.314
Minimum1945
Maximum2008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:45:07.010951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1945
Q11945
median1967
Q31968
95-th percentile1983
Maximum2008
Range63
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.983343
Coefficient of variation (CV)0.0071259456
Kurtosis0.48617947
Mean1962.314
Median Absolute Deviation (MAD)5
Skewness0.38482867
Sum237440
Variance195.53388
MonotonicityNot monotonic
2023-12-11T08:45:07.147380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1945 39
32.2%
1968 31
25.6%
1967 17
14.0%
1972 5
 
4.1%
1966 4
 
3.3%
1965 3
 
2.5%
1983 2
 
1.7%
1969 2
 
1.7%
1971 2
 
1.7%
1975 2
 
1.7%
Other values (14) 14
 
11.6%
ValueCountFrequency (%)
1945 39
32.2%
1948 1
 
0.8%
1959 1
 
0.8%
1962 1
 
0.8%
1965 3
 
2.5%
1966 4
 
3.3%
1967 17
14.0%
1968 31
25.6%
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%
1986 1
0.8%
1984 1
0.8%
1983 2
1.7%
1978 1
0.8%
1977 1
0.8%
1976 1
0.8%

관할기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
남해군 건설교통과
121 

Length

Max length9
Median length9
Mean length9
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 121
100.0%

Length

2023-12-11T08:45:07.276681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:45:07.368326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남해군 121
50.0%
건설교통과 121
50.0%

관리기관연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
055-860-3304
121 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row055-860-3304
2nd row055-860-3304
3rd row055-860-3304
4th row055-860-3304
5th row055-860-3304

Common Values

ValueCountFrequency (%)
055-860-3304 121
100.0%

Length

2023-12-11T08:45:07.456029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:45:07.539521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
055-860-3304 121
100.0%

Interactions

2023-12-11T08:45:01.980550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:57.969234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.569531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.197618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.788245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.494301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.237680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:02.078320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.048401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.702216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.279875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.891954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.597114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.349578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:02.183741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.137669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.793746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.374814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.002258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.734594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.444489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:02.283861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.213183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.863577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.460158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.119157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.827638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.525980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:02.378566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.298952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.945156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.548449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.229930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.928652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.643928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:02.741016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.390348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.022823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.614901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.312997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.022121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.746881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:02.868572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:58.474227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.112348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:59.701083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:00.403978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.135435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:01.857602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:45:07.613072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호유역면적(㏊)유효저수용량( ㎥)댐높이(m)댐길이(m)순관개면적(㏊)몽리면적준공년도
번호1.0000.0000.0000.0000.3810.2660.260
유역면적(㏊)0.0001.0000.5620.2450.4970.4810.000
유효저수용량( ㎥)0.0000.5621.0000.7600.7800.9090.655
댐높이(m)0.0000.2450.7601.0000.5920.5630.608
댐길이(m)0.3810.4970.7800.5921.0000.6870.877
순관개면적(㏊)몽리면적0.2660.4810.9090.5630.6871.0000.597
준공년도0.2600.0000.6550.6080.8770.5971.000
2023-12-11T08:45:07.732560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호유역면적(㏊)유효저수용량( ㎥)댐높이(m)댐길이(m)순관개면적(㏊)몽리면적준공년도
번호1.000-0.039-0.0050.1450.1890.0690.181
유역면적(㏊)-0.0391.0000.2910.1090.0460.2730.064
유효저수용량( ㎥)-0.0050.2911.0000.5610.2350.6320.109
댐높이(m)0.1450.1090.5611.000-0.0980.4250.342
댐길이(m)0.1890.0460.235-0.0981.0000.173-0.235
순관개면적(㏊)몽리면적0.0690.2730.6320.4250.1731.0000.152
준공년도0.1810.0640.1090.342-0.2350.1521.000

Missing values

2023-12-11T08:45:03.053914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:45:03.225488image/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(㏊)유효저수용량( ㎥)댐높이(m)댐길이(m)순관개면적(㏊)몽리면적준공년도관할기관명관리기관연락처
01상심천경상남도 남해군 남해읍 심천리1243,122566.043.712.56010.01959남해군 건설교통과055-860-3304
12모산경상남도 남해군 남해읍 심천리550-223.04.163.01759.81945남해군 건설교통과055-860-3304
23신촌경상남도 남해군 남해읍 평현리527-135.07.225.51405.71945남해군 건설교통과055-860-3304
34대입현1경상남도 남해군 남해읍 입현리117531.019.06.3955.01945남해군 건설교통과055-860-3304
45대입현2경상남도 남해군 남해읍 입현리10517.015.5811.0805.01972남해군 건설교통과055-860-3304
56토촌경상남도 남해군 남해읍 입현리77228.016.24.512711.01945남해군 건설교통과055-860-3304
67섬호경상남도 남해군 남해읍 입현리1406.07.446.0804.91945남해군 건설교통과055-860-3304
78외금1경상남도 남해군 남해읍 평리467-176.014.25.0319.01968남해군 건설교통과055-860-3304
89내금경상남도 남해군 남해읍 평리5216.026.49.07211.01972남해군 건설교통과055-860-3304
910외금 2경상남도 남해군 남해읍 평리1578-176.014.25.0319.01968남해군 건설교통과055-860-3304
번호저수지명소재지유역면적(㏊)유효저수용량( ㎥)댐높이(m)댐길이(m)순관개면적(㏊)몽리면적준공년도관할기관명관리기관연락처
111112사포경상남도 남해군 창선면 광천리15135.09.895.01404.01967남해군 건설교통과055-860-3304
112113지족경상남도 남해군 창선면 지족리2168.07.028.0403.01968남해군 건설교통과055-860-3304
113114연곡경상남도 남해군 창선면 오용리112762.030.07.01406.41968남해군 건설교통과055-860-3304
114115가인경상남도 남해군 창선면 가인리산207-722.012.89.0704.11968남해군 건설교통과055-860-3304
115116단항1경상남도 남해군 창선면 대벽리431-115.06.056.01204.51968남해군 건설교통과055-860-3304
116117단항2경상남도 남해군 창선면 대벽리33828.02.05.01203.01945남해군 건설교통과055-860-3304
117118대곡2경상남도 남해군 창선면 진동리739-263.08.255.08015.51976남해군 건설교통과055-860-3304
118119청산경상남도 남해군 창선면 지족리13-222.08.76.0605.51968남해군 건설교통과055-860-3304
119120광천경상남도 남해군 창선면 광천리886-497.06.954.0707.51945남해군 건설교통과055-860-3304
120121독망골경상남도 남해군 창선면 부윤리산23-240.0195.016.312430.02007남해군 건설교통과055-860-3304