Overview

Dataset statistics

Number of variables11
Number of observations128
Missing cells40
Missing cells (%)2.8%
Duplicate rows1
Duplicate rows (%)0.8%
Total size in memory11.9 KiB
Average record size in memory95.0 B

Variable types

Text2
Numeric6
Categorical3

Dataset

Description경상남도 의령군에 위치한 저수지명, 주소, 유역면적, 수혜면적, 준공년도, 제당높이, 제당길이, 관리기관명 및 연락처 등 저수지에 관한 정보입니다.
Author경상남도 의령군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15031904

Alerts

Dataset has 1 (0.8%) duplicate rowsDuplicates
관리기관명 is highly overall correlated with 유역면적(ha) and 7 other fieldsHigh correlation
데이터기준일 is highly overall correlated with 유역면적(ha) and 7 other fieldsHigh correlation
관리기관전화번호 is highly overall correlated with 유역면적(ha) and 7 other fieldsHigh correlation
유역면적(ha) is highly overall correlated with 관리기관명 and 2 other fieldsHigh correlation
수혜면적(ha) is highly overall correlated with 총저수량(천톤) and 4 other fieldsHigh correlation
총저수량(천톤) is highly overall correlated with 수혜면적(ha) and 3 other fieldsHigh correlation
축조연도 is highly overall correlated with 관리기관명 and 2 other fieldsHigh correlation
제당높이(m) is highly overall correlated with 관리기관명 and 2 other fieldsHigh correlation
제당길이(m) is highly overall correlated with 수혜면적(ha) and 3 other fieldsHigh correlation
관리기관명 is highly imbalanced (76.2%)Imbalance
관리기관전화번호 is highly imbalanced (76.2%)Imbalance
데이터기준일 is highly imbalanced (76.2%)Imbalance
저수지명 has 5 (3.9%) missing valuesMissing
주소 has 5 (3.9%) missing valuesMissing
유역면적(ha) has 5 (3.9%) missing valuesMissing
수혜면적(ha) has 5 (3.9%) missing valuesMissing
총저수량(천톤) has 5 (3.9%) missing valuesMissing
축조연도 has 5 (3.9%) missing valuesMissing
제당높이(m) has 5 (3.9%) missing valuesMissing
제당길이(m) has 5 (3.9%) missing valuesMissing

Reproduction

Analysis started2023-12-10 23:51:20.991777
Analysis finished2023-12-10 23:51:25.070582
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

저수지명
Text

MISSING 

Distinct120
Distinct (%)97.6%
Missing5
Missing (%)3.9%
Memory size1.1 KiB
2023-12-11T08:51:25.359438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.203252
Min length2

Characters and Unicode

Total characters271
Distinct characters106
Distinct categories1 ?
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 (%)95.1%

Sample

1st row내산다
2nd row오령지
3rd row외산다
4th row척곡
5th row수암
ValueCountFrequency (%)
상촌 2
 
1.6%
죽전 2
 
1.6%
사곡 2
 
1.6%
구오목 1
 
0.8%
월현 1
 
0.8%
내곡 1
 
0.8%
각곡 1
 
0.8%
양동 1
 
0.8%
웅곡 1
 
0.8%
상신기 1
 
0.8%
Other values (110) 110
89.4%
2023-12-11T08:51:25.817941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
곡 35
 
12.9%
상 10
 
3.7%
산 10
 
3.7%
촌 9
 
3.3%
수 9
 
3.3%
동 7
 
2.6%
신 6
 
2.2%
오 6
 
2.2%
하 5
 
1.8%
전 5
 
1.8%
Other values (96) 169
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 271
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
곡 35
 
12.9%
상 10
 
3.7%
산 10
 
3.7%
촌 9
 
3.3%
수 9
 
3.3%
동 7
 
2.6%
신 6
 
2.2%
오 6
 
2.2%
하 5
 
1.8%
전 5
 
1.8%
Other values (96) 169
62.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 267
98.5%
Han 4
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
곡 35
 
13.1%
상 10
 
3.7%
산 10
 
3.7%
촌 9
 
3.4%
수 9
 
3.4%
동 7
 
2.6%
신 6
 
2.2%
오 6
 
2.2%
하 5
 
1.9%
전 5
 
1.9%
Other values (94) 165
61.8%
Han
ValueCountFrequency (%)
小 2
50.0%
大 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 267
98.5%
CJK 4
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
곡 35
 
13.1%
상 10
 
3.7%
산 10
 
3.7%
촌 9
 
3.4%
수 9
 
3.4%
동 7
 
2.6%
신 6
 
2.2%
오 6
 
2.2%
하 5
 
1.9%
전 5
 
1.9%
Other values (94) 165
61.8%
CJK
ValueCountFrequency (%)
小 2
50.0%
大 2
50.0%

주소
Text

MISSING 

Distinct122
Distinct (%)99.2%
Missing5
Missing (%)3.9%
Memory size1.1 KiB
2023-12-11T08:51:26.233622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length20.796748
Min length18

Characters and Unicode

Total characters2558
Distinct characters103
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)98.4%

Sample

1st row경상남도 의령군 의령읍 상리 255
2nd row경상남도 의령군 의령읍 상리 255
3rd row경상남도 의령군 의령읍 상리 383-2
4th row경상남도 의령군 의령읍 중리 568-2
5th row경상남도 의령군 의령읍 하리 675-2
ValueCountFrequency (%)
경상남도 123
20.0%
의령군 123
20.0%
화정면 18
 
2.9%
지정면 13
 
2.1%
정곡면 13
 
2.1%
의령읍 12
 
2.0%
용덕면 12
 
2.0%
대의면 12
 
2.0%
칠곡면 12
 
2.0%
부림면 10
 
1.6%
Other values (193) 267
43.4%
2023-12-11T08:51:26.797490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
493
19.3%
의 147
 
5.7%
상 140
 
5.5%
령 135
 
5.3%
도 126
 
4.9%
경 124
 
4.8%
남 124
 
4.8%
군 123
 
4.8%
리 123
 
4.8%
면 111
 
4.3%
Other values (93) 912
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1587
62.0%
Space Separator 493
 
19.3%
Decimal Number 420
 
16.4%
Dash Punctuation 58
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 147
 
9.3%
상 140
 
8.8%
령 135
 
8.5%
도 126
 
7.9%
경 124
 
7.8%
남 124
 
7.8%
군 123
 
7.8%
리 123
 
7.8%
면 111
 
7.0%
곡 48
 
3.0%
Other values (81) 386
24.3%
Decimal Number
ValueCountFrequency (%)
1 73
17.4%
2 49
11.7%
3 48
11.4%
5 41
9.8%
8 39
9.3%
4 38
9.0%
9 37
8.8%
7 36
8.6%
6 34
8.1%
0 25
 
6.0%
Space Separator
ValueCountFrequency (%)
493
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1587
62.0%
Common 971
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 147
 
9.3%
상 140
 
8.8%
령 135
 
8.5%
도 126
 
7.9%
경 124
 
7.8%
남 124
 
7.8%
군 123
 
7.8%
리 123
 
7.8%
면 111
 
7.0%
곡 48
 
3.0%
Other values (81) 386
24.3%
Common
ValueCountFrequency (%)
493
50.8%
1 73
 
7.5%
- 58
 
6.0%
2 49
 
5.0%
3 48
 
4.9%
5 41
 
4.2%
8 39
 
4.0%
4 38
 
3.9%
9 37
 
3.8%
7 36
 
3.7%
Other values (2) 59
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1587
62.0%
ASCII 971
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
493
50.8%
1 73
 
7.5%
- 58
 
6.0%
2 49
 
5.0%
3 48
 
4.9%
5 41
 
4.2%
8 39
 
4.0%
4 38
 
3.9%
9 37
 
3.8%
7 36
 
3.7%
Other values (2) 59
 
6.1%
Hangul
ValueCountFrequency (%)
의 147
 
9.3%
상 140
 
8.8%
령 135
 
8.5%
도 126
 
7.9%
경 124
 
7.8%
남 124
 
7.8%
군 123
 
7.8%
리 123
 
7.8%
면 111
 
7.0%
곡 48
 
3.0%
Other values (81) 386
24.3%

유역면적(ha)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct69
Distinct (%)56.1%
Missing5
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean58.084553
Minimum0.1
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T08:51:26.938738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.01
Q128
median42
Q365.25
95-th percentile164.5
Maximum460
Range459.9
Interquartile range (IQR)37.25

Descriptive statistics

Standard deviation61.412114
Coefficient of variation (CV)1.0572882
Kurtosis16.836475
Mean58.084553
Median Absolute Deviation (MAD)20
Skewness3.3977126
Sum7144.4
Variance3771.4477
MonotonicityNot monotonic
2023-12-11T08:51:27.105634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 6
 
4.7%
45.0 6
 
4.7%
1.0 5
 
3.9%
25.0 4
 
3.1%
32.0 3
 
2.3%
80.0 3
 
2.3%
62.0 3
 
2.3%
37.0 3
 
2.3%
1.2 3
 
2.3%
85.0 3
 
2.3%
Other values (59) 84
65.6%
(Missing) 5
 
3.9%
ValueCountFrequency (%)
0.1 1
 
0.8%
0.4 1
 
0.8%
1.0 5
3.9%
1.1 2
 
1.6%
1.2 3
2.3%
2.0 1
 
0.8%
7.5 1
 
0.8%
8.0 1
 
0.8%
13.0 1
 
0.8%
15.0 2
 
1.6%
ValueCountFrequency (%)
460.0 1
0.8%
320.0 1
0.8%
220.0 1
0.8%
200.0 1
0.8%
175.0 1
0.8%
170.0 1
0.8%
165.0 1
0.8%
160.0 1
0.8%
150.0 1
0.8%
140.0 1
0.8%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct68
Distinct (%)55.3%
Missing5
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean7.8934959
Minimum1.3
Maximum29.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T08:51:27.248474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2.02
Q15
median6.8
Q310
95-th percentile18.97
Maximum29.3
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.9609415
Coefficient of variation (CV)0.62848471
Kurtosis3.0566173
Mean7.8934959
Median Absolute Deviation (MAD)2.2
Skewness1.5702615
Sum970.9
Variance24.610941
MonotonicityNot monotonic
2023-12-11T08:51:27.463260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 14
 
10.9%
8.0 8
 
6.2%
10.0 7
 
5.5%
7.0 6
 
4.7%
6.0 5
 
3.9%
15.0 5
 
3.9%
20.0 4
 
3.1%
6.8 2
 
1.6%
8.4 2
 
1.6%
4.0 2
 
1.6%
Other values (58) 68
53.1%
(Missing) 5
 
3.9%
ValueCountFrequency (%)
1.3 1
0.8%
1.5 2
1.6%
1.7 1
0.8%
1.8 1
0.8%
2.0 2
1.6%
2.2 1
0.8%
2.3 1
0.8%
2.4 1
0.8%
2.5 1
0.8%
2.7 1
0.8%
ValueCountFrequency (%)
29.3 1
 
0.8%
23.6 1
 
0.8%
20.0 4
3.1%
19.0 1
 
0.8%
18.7 1
 
0.8%
17.0 1
 
0.8%
15.0 5
3.9%
14.2 1
 
0.8%
13.1 1
 
0.8%
13.0 2
 
1.6%

총저수량(천톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct78
Distinct (%)63.4%
Missing5
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean15.595122
Minimum0.5
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T08:51:27.652613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.91
Q13.05
median9.6
Q319
95-th percentile39.61
Maximum262
Range261.5
Interquartile range (IQR)15.95

Descriptive statistics

Standard deviation26.312242
Coefficient of variation (CV)1.6872098
Kurtosis63.660271
Mean15.595122
Median Absolute Deviation (MAD)7.5
Skewness7.0508502
Sum1918.2
Variance692.33407
MonotonicityNot monotonic
2023-12-11T08:51:27.793391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.0 5
 
3.9%
3.0 5
 
3.9%
6.0 4
 
3.1%
18.0 4
 
3.1%
1.1 4
 
3.1%
14.0 4
 
3.1%
7.0 3
 
2.3%
10.0 3
 
2.3%
1.0 3
 
2.3%
26.0 3
 
2.3%
Other values (68) 85
66.4%
(Missing) 5
 
3.9%
ValueCountFrequency (%)
0.5 2
1.6%
0.6 2
1.6%
0.7 1
 
0.8%
0.9 2
1.6%
1.0 3
2.3%
1.1 4
3.1%
1.2 3
2.3%
1.3 2
1.6%
1.5 1
 
0.8%
1.6 1
 
0.8%
ValueCountFrequency (%)
262.0 1
0.8%
79.0 1
0.8%
65.0 1
0.8%
49.9 1
0.8%
49.0 1
0.8%
47.1 1
0.8%
39.9 1
0.8%
37.0 1
0.8%
36.6 1
0.8%
36.5 1
0.8%

축조연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)33.3%
Missing5
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1962.2033
Minimum1930
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T08:51:27.913856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1930
5-th percentile1937.1
Q11956
median1965
Q31970
95-th percentile1978
Maximum2012
Range82
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.122972
Coefficient of variation (CV)0.0071975074
Kurtosis1.996976
Mean1962.2033
Median Absolute Deviation (MAD)6
Skewness0.2257998
Sum241351
Variance199.45835
MonotonicityNot monotonic
2023-12-11T08:51:28.059913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1968 13
 
10.2%
1970 8
 
6.2%
1967 7
 
5.5%
1959 6
 
4.7%
1972 6
 
4.7%
1963 5
 
3.9%
1964 5
 
3.9%
1956 5
 
3.9%
1969 5
 
3.9%
1961 4
 
3.1%
Other values (31) 59
46.1%
(Missing) 5
 
3.9%
ValueCountFrequency (%)
1930 2
1.6%
1935 1
 
0.8%
1936 2
1.6%
1937 2
1.6%
1938 1
 
0.8%
1939 3
2.3%
1940 3
2.3%
1942 3
2.3%
1943 1
 
0.8%
1944 1
 
0.8%
ValueCountFrequency (%)
2012 1
 
0.8%
2011 1
 
0.8%
2005 1
 
0.8%
1983 1
 
0.8%
1981 1
 
0.8%
1978 3
2.3%
1976 2
1.6%
1975 2
1.6%
1974 1
 
0.8%
1973 2
1.6%

제당높이(m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)34.1%
Missing5
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean7.3300813
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T08:51:28.210448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15.35
median7
Q38.6
95-th percentile12
Maximum21
Range19
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.8417247
Coefficient of variation (CV)0.38767983
Kurtosis4.8174229
Mean7.3300813
Median Absolute Deviation (MAD)1.6
Skewness1.660849
Sum901.6
Variance8.0753992
MonotonicityNot monotonic
2023-12-11T08:51:28.372912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5.0 18
14.1%
7.0 17
13.3%
6.0 11
 
8.6%
8.0 8
 
6.2%
10.0 8
 
6.2%
6.5 6
 
4.7%
4.0 5
 
3.9%
12.0 5
 
3.9%
9.0 4
 
3.1%
5.6 2
 
1.6%
Other values (32) 39
30.5%
(Missing) 5
 
3.9%
ValueCountFrequency (%)
2.0 1
 
0.8%
3.0 1
 
0.8%
3.2 1
 
0.8%
3.5 1
 
0.8%
4.0 5
 
3.9%
4.3 1
 
0.8%
4.5 1
 
0.8%
4.8 1
 
0.8%
5.0 18
14.1%
5.2 1
 
0.8%
ValueCountFrequency (%)
21.0 1
 
0.8%
18.0 1
 
0.8%
14.0 2
 
1.6%
12.6 2
 
1.6%
12.0 5
3.9%
11.0 2
 
1.6%
10.0 8
6.2%
9.8 1
 
0.8%
9.7 1
 
0.8%
9.5 1
 
0.8%

제당길이(m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct73
Distinct (%)59.3%
Missing5
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean73.964228
Minimum15
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T08:51:28.532748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile34.1
Q148
median68
Q390
95-th percentile134
Maximum262
Range247
Interquartile range (IQR)42

Descriptive statistics

Standard deviation35.883178
Coefficient of variation (CV)0.48514234
Kurtosis5.5347423
Mean73.964228
Median Absolute Deviation (MAD)21
Skewness1.6660866
Sum9097.6
Variance1287.6025
MonotonicityNot monotonic
2023-12-11T08:51:28.660823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 6
 
4.7%
90.0 5
 
3.9%
48.0 5
 
3.9%
66.0 4
 
3.1%
55.0 3
 
2.3%
85.0 3
 
2.3%
82.0 3
 
2.3%
68.0 3
 
2.3%
87.0 3
 
2.3%
50.0 3
 
2.3%
Other values (63) 85
66.4%
(Missing) 5
 
3.9%
ValueCountFrequency (%)
15.0 1
 
0.8%
20.0 1
 
0.8%
26.0 1
 
0.8%
27.0 1
 
0.8%
31.0 2
1.6%
34.0 1
 
0.8%
35.0 3
2.3%
36.0 2
1.6%
37.0 1
 
0.8%
39.0 1
 
0.8%
ValueCountFrequency (%)
262.0 1
0.8%
180.0 1
0.8%
156.0 1
0.8%
155.0 1
0.8%
137.0 1
0.8%
135.0 1
0.8%
134.0 2
1.6%
130.5 1
0.8%
130.0 2
1.6%
129.0 1
0.8%

관리기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
경상남도 의령군청
123 
<NA>
 
5

Length

Max length9
Median length9
Mean length8.8046875
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도 의령군청
2nd row경상남도 의령군청
3rd row경상남도 의령군청
4th row경상남도 의령군청
5th row경상남도 의령군청

Common Values

ValueCountFrequency (%)
경상남도 의령군청 123
96.1%
<NA> 5
 
3.9%

Length

2023-12-11T08:51:28.799526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:51:28.938370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 123
49.0%
의령군청 123
49.0%
na 5
 
2.0%

관리기관전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
055-570-3623
123 
<NA>
 
5

Length

Max length12
Median length12
Mean length11.6875
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row055-570-3623
2nd row055-570-3623
3rd row055-570-3623
4th row055-570-3623
5th row055-570-3623

Common Values

ValueCountFrequency (%)
055-570-3623 123
96.1%
<NA> 5
 
3.9%

Length

2023-12-11T08:51:29.071659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:51:29.186207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
055-570-3623 123
96.1%
na 5
 
3.9%

데이터기준일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2021-06-10
123 
<NA>
 
5

Length

Max length10
Median length10
Mean length9.765625
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-10
2nd row2021-06-10
3rd row2021-06-10
4th row2021-06-10
5th row2021-06-10

Common Values

ValueCountFrequency (%)
2021-06-10 123
96.1%
<NA> 5
 
3.9%

Length

2023-12-11T08:51:29.574530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:51:29.683067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-10 123
96.1%
na 5
 
3.9%

Interactions

2023-12-11T08:51:24.059599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:21.430662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.007952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.487482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.907323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.564298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:24.144483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:21.530475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.090193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.551036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.986909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.643130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:24.224413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:21.617765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.170173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.623439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.289061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.731092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:24.305134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:21.705068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.256288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.693387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.353422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.824081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:24.375894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:21.818381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.326247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.762464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.423454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.896086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:24.466824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:21.918209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.411118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:22.840909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.499439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:51:23.979060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:51:29.743854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유역면적(ha)수혜면적(ha)총저수량(천톤)축조연도제당높이(m)제당길이(m)
유역면적(ha)1.0000.3800.3040.4020.3020.723
수혜면적(ha)0.3801.0000.7260.0000.6850.439
총저수량(천톤)0.3040.7261.0000.5330.7510.558
축조연도0.4020.0000.5331.0000.4860.393
제당높이(m)0.3020.6850.7510.4861.0000.551
제당길이(m)0.7230.4390.5580.3930.5511.000
2023-12-11T08:51:29.838838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관명데이터기준일관리기관전화번호
관리기관명1.0001.0001.000
데이터기준일1.0001.0001.000
관리기관전화번호1.0001.0001.000
2023-12-11T08:51:29.934098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유역면적(ha)수혜면적(ha)총저수량(천톤)축조연도제당높이(m)제당길이(m)관리기관명관리기관전화번호데이터기준일
유역면적(ha)1.0000.3190.282-0.0400.3380.2831.0001.0001.000
수혜면적(ha)0.3191.0000.502-0.0150.2470.5101.0001.0001.000
총저수량(천톤)0.2820.5021.0000.1000.4050.5001.0001.0001.000
축조연도-0.040-0.0150.1001.0000.2750.0211.0001.0001.000
제당높이(m)0.3380.2470.4050.2751.0000.2101.0001.0001.000
제당길이(m)0.2830.5100.5000.0210.2101.0001.0001.0001.000
관리기관명1.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기관전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
데이터기준일1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T08:51:24.626090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:51:24.793027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T08:51:24.946668image/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

저수지명주소유역면적(ha)수혜면적(ha)총저수량(천톤)축조연도제당높이(m)제당길이(m)관리기관명관리기관전화번호데이터기준일
0내산다경상남도 의령군 의령읍 상리 25530.010.0262.0201121.0155.0경상남도 의령군청055-570-36232021-06-10
1오령지경상남도 의령군 의령읍 상리 25555.017.026.019378.798.0경상남도 의령군청055-570-36232021-06-10
2외산다경상남도 의령군 의령읍 상리 383-247.06.513.019687.880.0경상남도 의령군청055-570-36232021-06-10
3척곡경상남도 의령군 의령읍 중리 568-2175.020.036.419587.081.0경상남도 의령군청055-570-36232021-06-10
4수암경상남도 의령군 의령읍 하리 675-2320.020.036.619429.8107.0경상남도 의령군청055-570-36232021-06-10
5장재경상남도 의령군 의령읍 하리 87858.013.018.019689.045.0경상남도 의령군청055-570-36232021-06-10
6대산경상남도 의령군 의령읍 대산리 1130-215.010.019.419676.153.0경상남도 의령군청055-570-36232021-06-10
7우수곡경상남도 의령군 의령읍 서동리 13042.010.010.6194210.098.0경상남도 의령군청055-570-36232021-06-10
8무상경상남도 의령군 의령읍 무전리 85722.010.020.019748.096.0경상남도 의령군청055-570-36232021-06-10
9오감경상남도 의령군 의령읍 대산리 116223.05.08.719717.056.0경상남도 의령군청055-570-36232021-06-10
저수지명주소유역면적(ha)수혜면적(ha)총저수량(천톤)축조연도제당높이(m)제당길이(m)관리기관명관리기관전화번호데이터기준일
118덕천경상남도 의령군 유곡면 덕천리 17850.015.018.219678.090.0경상남도 의령군청055-570-36232021-06-10
119오목경상남도 의령군 유곡면 상곡리 400-1100.020.018.019638.082.0경상남도 의령군청055-570-36232021-06-10
120신촌경상남도 의령군 유곡면 신촌리 384-265.57.06.819677.043.0경상남도 의령군청055-570-36232021-06-10
121구오목경상남도 의령군 유곡면 오목리 30530.08.05.9196412.031.0경상남도 의령군청055-570-36232021-06-10
122덕천소경상남도 의령군 유곡면 덕천리 129-248.012.70.719787.015.0경상남도 의령군청055-570-36232021-06-10
123<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
124<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
125<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
126<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
127<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

저수지명주소유역면적(ha)수혜면적(ha)총저수량(천톤)축조연도제당높이(m)제당길이(m)관리기관명관리기관전화번호데이터기준일# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>5