Overview

Dataset statistics

Number of variables18
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory159.3 B

Variable types

Categorical7
Text1
Numeric8
DateTime2

Dataset

Description여수시 관내 방조제 개소, 면적, 연장, 갑문 수, 조수량, 제고, 여유고, 설치연도, 국가 편입년도, 관리주체 등 자료 제공합니다.
URLhttps://www.data.go.kr/data/3079935/fileData.do

Alerts

시군 has constant value ""Constant
시 has constant value ""Constant
면적 -구역 (ha) is highly overall correlated with 면적- 몽리 (ha) and 7 other fieldsHigh correlation
면적- 몽리 (ha) is highly overall correlated with 면적 -구역 (ha) and 7 other fieldsHigh correlation
방조제- 연장(m) is highly overall correlated with 면적 -구역 (ha) and 6 other fieldsHigh correlation
배수갑문(연수) is highly overall correlated with 면적 -구역 (ha) and 5 other fieldsHigh correlation
포 용 조수량(천m3) is highly overall correlated with 면적 -구역 (ha) and 4 other fieldsHigh correlation
제 고- 최고(m) is highly overall correlated with 면적 -구역 (ha) and 3 other fieldsHigh correlation
제고 - 평균(m) is highly overall correlated with 제 고- 최고(m) and 2 other fieldsHigh correlation
여유고 (m) is highly overall correlated with 제고 - 평균(m) and 2 other fieldsHigh correlation
관리구분 is highly overall correlated with 면적 -구역 (ha) and 5 other fieldsHigh correlation
관리자명 is highly overall correlated with 면적 -구역 (ha) and 5 other fieldsHigh correlation
방조제- 조(m) is highly overall correlated with 면적 -구역 (ha) and 5 other fieldsHigh correlation
배수갑문(개소) is highly overall correlated with 면적- 몽리 (ha) and 3 other fieldsHigh correlation
방조제- 조(m) is highly imbalanced (75.8%)Imbalance
배수갑문(개소) is highly imbalanced (59.8%)Imbalance
지구명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:13:04.009119
Analysis finished2023-12-12 09:13:12.610640
Duration8.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
여수시
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여수시 25
100.0%

Length

2023-12-12T18:13:12.697415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:12.797877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여수시 25
100.0%

관리구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
시관리
19 
도관리
4 
국가관리
2 

Length

Max length4
Median length3
Mean length3.08
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국가관리
2nd row국가관리
3rd row도관리
4th row도관리
5th row도관리

Common Values

ValueCountFrequency (%)
시관리 19
76.0%
도관리 4
 
16.0%
국가관리 2
 
8.0%

Length

2023-12-12T18:13:12.932913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:13.035023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시관리 19
76.0%
도관리 4
 
16.0%
국가관리 2
 
8.0%

관리자명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
여수시장
21 
순천지사
4 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row순천지사
2nd row순천지사
3rd row여수시장
4th row여수시장
5th row순천지사

Common Values

ValueCountFrequency (%)
여수시장 21
84.0%
순천지사 4
 
16.0%

Length

2023-12-12T18:13:13.150128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:13.255460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여수시장 21
84.0%
순천지사 4
 
16.0%

지구명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T18:13:13.442788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters50
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row관기
2nd row대포
3rd row옥적
4th row묘도
5th row조화
ValueCountFrequency (%)
관기 1
 
4.0%
반월 1
 
4.0%
제도 1
 
4.0%
운두 1
 
4.0%
용주 1
 
4.0%
오천 1
 
4.0%
안정 1
 
4.0%
세포 1
 
4.0%
석교 1
 
4.0%
마상 1
 
4.0%
Other values (15) 15
60.0%
2023-12-12T18:13:13.828097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
포 4
 
8.0%
도 3
 
6.0%
두 2
 
4.0%
천 2
 
4.0%
대 2
 
4.0%
적 2
 
4.0%
봉 2
 
4.0%
화 2
 
4.0%
곡 2
 
4.0%
마 1
 
2.0%
Other values (28) 28
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
포 4
 
8.0%
도 3
 
6.0%
두 2
 
4.0%
천 2
 
4.0%
대 2
 
4.0%
적 2
 
4.0%
봉 2
 
4.0%
화 2
 
4.0%
곡 2
 
4.0%
마 1
 
2.0%
Other values (28) 28
56.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
포 4
 
8.0%
도 3
 
6.0%
두 2
 
4.0%
천 2
 
4.0%
대 2
 
4.0%
적 2
 
4.0%
봉 2
 
4.0%
화 2
 
4.0%
곡 2
 
4.0%
마 1
 
2.0%
Other values (28) 28
56.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
포 4
 
8.0%
도 3
 
6.0%
두 2
 
4.0%
천 2
 
4.0%
대 2
 
4.0%
적 2
 
4.0%
봉 2
 
4.0%
화 2
 
4.0%
곡 2
 
4.0%
마 1
 
2.0%
Other values (28) 28
56.0%

시
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
전라남도 여수시
25 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 여수시
2nd row전라남도 여수시
3rd row전라남도 여수시
4th row전라남도 여수시
5th row전라남도 여수시

Common Values

ValueCountFrequency (%)
전라남도 여수시 25
100.0%

Length

2023-12-12T18:13:14.005682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:14.162340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 25
50.0%
여수시 25
50.0%

읍면
Categorical

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
화양
9 
율촌
6 
소라
5 
화정
2 
묘도
1 
Other values (2)
2 

Length

Max length2
Median length2
Mean length1.96
Min length1

Unique

Unique3 ?
Unique (%)12.0%

Sample

1st row소라
2nd row소라
3rd row화양
4th row묘도
5th row율촌

Common Values

ValueCountFrequency (%)
화양 9
36.0%
율촌 6
24.0%
소라 5
20.0%
화정 2
 
8.0%
묘도 1
 
4.0%
남 1
 
4.0%
돌산 1
 
4.0%

Length

2023-12-12T18:13:14.296552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:14.456294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화양 9
36.0%
율촌 6
24.0%
소라 5
20.0%
화정 2
 
8.0%
묘도 1
 
4.0%
남 1
 
4.0%
돌산 1
 
4.0%

면적 -구역 (ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.168
Minimum1
Maximum425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:14.620764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.2
Q19
median16.1
Q338
95-th percentile252.84
Maximum425
Range424
Interquartile range (IQR)29

Descriptive statistics

Standard deviation97.681701
Coefficient of variation (CV)2.0279377
Kurtosis10.861596
Mean48.168
Median Absolute Deviation (MAD)8.1
Skewness3.343881
Sum1204.2
Variance9541.7148
MonotonicityNot monotonic
2023-12-12T18:13:14.783073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
9.0 2
 
8.0%
10.0 2
 
8.0%
39.0 2
 
8.0%
14.0 2
 
8.0%
301.0 1
 
4.0%
2.0 1
 
4.0%
8.0 1
 
4.0%
13.0 1
 
4.0%
1.0 1
 
4.0%
18.0 1
 
4.0%
Other values (11) 11
44.0%
ValueCountFrequency (%)
1.0 1
4.0%
2.0 1
4.0%
8.0 1
4.0%
8.4 1
4.0%
8.5 1
4.0%
9.0 2
8.0%
10.0 2
8.0%
13.0 1
4.0%
14.0 2
8.0%
16.1 1
4.0%
ValueCountFrequency (%)
425.0 1
4.0%
301.0 1
4.0%
60.2 1
4.0%
55.0 1
4.0%
39.0 2
8.0%
38.0 1
4.0%
35.0 1
4.0%
26.0 1
4.0%
25.0 1
4.0%
20.0 1
4.0%

면적- 몽리 (ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.868
Minimum1
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:14.934722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.52
Q14.4
median13
Q332
95-th percentile162.04
Maximum280
Range279
Interquartile range (IQR)27.6

Descriptive statistics

Standard deviation63.65278
Coefficient of variation (CV)1.8794372
Kurtosis10.423689
Mean33.868
Median Absolute Deviation (MAD)9
Skewness3.2070322
Sum846.7
Variance4051.6764
MonotonicityNot monotonic
2023-12-12T18:13:15.095089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2.0 2
 
8.0%
18.0 2
 
8.0%
3.0 1
 
4.0%
13.7 1
 
4.0%
1.4 1
 
4.0%
13.0 1
 
4.0%
1.0 1
 
4.0%
4.0 1
 
4.0%
10.0 1
 
4.0%
16.1 1
 
4.0%
Other values (13) 13
52.0%
ValueCountFrequency (%)
1.0 1
4.0%
1.4 1
4.0%
2.0 2
8.0%
3.0 1
4.0%
4.0 1
4.0%
4.4 1
4.0%
7.0 1
4.0%
8.4 1
4.0%
8.5 1
4.0%
9.0 1
4.0%
ValueCountFrequency (%)
280.0 1
4.0%
188.0 1
4.0%
58.2 1
4.0%
52.0 1
4.0%
39.0 1
4.0%
38.0 1
4.0%
32.0 1
4.0%
20.0 1
4.0%
18.0 2
8.0%
16.1 1
4.0%

방조제- 조(m)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
1
24 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 24
96.0%
2 1
 
4.0%

Length

2023-12-12T18:13:15.271799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:15.423446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 24
96.0%
2 1
 
4.0%

방조제- 연장(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.48
Minimum70
Maximum2150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:15.571913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile88
Q1160
median232
Q3289
95-th percentile1169.8
Maximum2150
Range2080
Interquartile range (IQR)129

Descriptive statistics

Standard deviation446.86091
Coefficient of variation (CV)1.2823144
Kurtosis12.043384
Mean348.48
Median Absolute Deviation (MAD)72
Skewness3.406524
Sum8712
Variance199684.68
MonotonicityNot monotonic
2023-12-12T18:13:15.746384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
160 3
 
12.0%
270 2
 
8.0%
497 1
 
4.0%
150 1
 
4.0%
441 1
 
4.0%
138 1
 
4.0%
80 1
 
4.0%
316 1
 
4.0%
250 1
 
4.0%
300 1
 
4.0%
Other values (12) 12
48.0%
ValueCountFrequency (%)
70 1
 
4.0%
80 1
 
4.0%
120 1
 
4.0%
138 1
 
4.0%
150 1
 
4.0%
160 3
12.0%
178 1
 
4.0%
203 1
 
4.0%
220 1
 
4.0%
225 1
 
4.0%
ValueCountFrequency (%)
2150 1
4.0%
1338 1
4.0%
497 1
4.0%
441 1
4.0%
316 1
4.0%
300 1
4.0%
289 1
4.0%
270 2
8.0%
257 1
4.0%
250 1
4.0%

배수갑문(개소)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
1
23 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 23
92.0%
2 2
 
8.0%

Length

2023-12-12T18:13:15.908906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:16.034375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 23
92.0%
2 2
 
8.0%

배수갑문(연수)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.44
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:16.156033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile10.8
Maximum18
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.9272552
Coefficient of variation (CV)1.141644
Kurtosis8.0044278
Mean3.44
Median Absolute Deviation (MAD)1
Skewness2.6919521
Sum86
Variance15.423333
MonotonicityNot monotonic
2023-12-12T18:13:16.273857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 9
36.0%
2 7
28.0%
4 4
16.0%
6 2
 
8.0%
12 1
 
4.0%
18 1
 
4.0%
5 1
 
4.0%
ValueCountFrequency (%)
1 9
36.0%
2 7
28.0%
4 4
16.0%
5 1
 
4.0%
6 2
 
8.0%
12 1
 
4.0%
18 1
 
4.0%
ValueCountFrequency (%)
18 1
 
4.0%
12 1
 
4.0%
6 2
 
8.0%
5 1
 
4.0%
4 4
16.0%
2 7
28.0%
1 9
36.0%

포 용 조수량(천m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean801.28
Minimum2
Maximum5946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:16.416950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile28.4
Q199
median198
Q3536
95-th percentile5004.4
Maximum5946
Range5944
Interquartile range (IQR)437

Descriptive statistics

Standard deviation1584.4702
Coefficient of variation (CV)1.9774239
Kurtosis7.8806288
Mean801.28
Median Absolute Deviation (MAD)163
Skewness2.9262626
Sum20032
Variance2510545.8
MonotonicityNot monotonic
2023-12-12T18:13:16.554525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
198 2
 
8.0%
5801 1
 
4.0%
28 1
 
4.0%
68 1
 
4.0%
35 1
 
4.0%
99 1
 
4.0%
291 1
 
4.0%
30 1
 
4.0%
317 1
 
4.0%
174 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
2 1
4.0%
28 1
4.0%
30 1
4.0%
35 1
4.0%
37 1
4.0%
68 1
4.0%
99 1
4.0%
104 1
4.0%
112 1
4.0%
150 1
4.0%
ValueCountFrequency (%)
5946 1
4.0%
5801 1
4.0%
1818 1
4.0%
1058 1
4.0%
952 1
4.0%
930 1
4.0%
536 1
4.0%
449 1
4.0%
422 1
4.0%
317 1
4.0%

제 고- 최고(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.968
Minimum2.4
Maximum5.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:16.682261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile2.84
Q13.4
median4
Q35
95-th percentile5.18
Maximum5.6
Range3.2
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.89243114
Coefficient of variation (CV)0.22490704
Kurtosis-1.0995913
Mean3.968
Median Absolute Deviation (MAD)1
Skewness0.17190432
Sum99.2
Variance0.79643333
MonotonicityNot monotonic
2023-12-12T18:13:16.796707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5.0 4
16.0%
4.0 4
16.0%
3.0 4
16.0%
3.5 3
12.0%
3.6 2
8.0%
4.5 2
8.0%
5.1 1
 
4.0%
5.2 1
 
4.0%
5.6 1
 
4.0%
3.4 1
 
4.0%
Other values (2) 2
8.0%
ValueCountFrequency (%)
2.4 1
 
4.0%
2.8 1
 
4.0%
3.0 4
16.0%
3.4 1
 
4.0%
3.5 3
12.0%
3.6 2
8.0%
4.0 4
16.0%
4.5 2
8.0%
5.0 4
16.0%
5.1 1
 
4.0%
ValueCountFrequency (%)
5.6 1
 
4.0%
5.2 1
 
4.0%
5.1 1
 
4.0%
5.0 4
16.0%
4.5 2
8.0%
4.0 4
16.0%
3.6 2
8.0%
3.5 3
12.0%
3.4 1
 
4.0%
3.0 4
16.0%

제고 - 평균(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.748
Minimum1.9
Maximum6.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:16.932333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2.34
Q13.1
median3.6
Q34.3
95-th percentile5.52
Maximum6.5
Range4.6
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.0916807
Coefficient of variation (CV)0.29127019
Kurtosis0.35347158
Mean3.748
Median Absolute Deviation (MAD)0.6
Skewness0.62076334
Sum93.7
Variance1.1917667
MonotonicityNot monotonic
2023-12-12T18:13:17.112774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4.0 3
 
12.0%
2.5 2
 
8.0%
3.2 2
 
8.0%
3.1 2
 
8.0%
5.0 1
 
4.0%
4.2 1
 
4.0%
3.0 1
 
4.0%
4.6 1
 
4.0%
6.5 1
 
4.0%
4.3 1
 
4.0%
Other values (10) 10
40.0%
ValueCountFrequency (%)
1.9 1
4.0%
2.3 1
4.0%
2.5 2
8.0%
2.7 1
4.0%
3.0 1
4.0%
3.1 2
8.0%
3.2 2
8.0%
3.4 1
4.0%
3.5 1
4.0%
3.6 1
4.0%
ValueCountFrequency (%)
6.5 1
 
4.0%
5.6 1
 
4.0%
5.2 1
 
4.0%
5.0 1
 
4.0%
4.6 1
 
4.0%
4.5 1
 
4.0%
4.3 1
 
4.0%
4.2 1
 
4.0%
4.0 3
12.0%
3.8 1
 
4.0%

여유고 (m)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3872
Minimum0.5
Maximum2.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:13:17.295121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.87
Q11.06
median1.25
Q31.65
95-th percentile2.19
Maximum2.4
Range1.9
Interquartile range (IQR)0.59

Descriptive statistics

Standard deviation0.46853957
Coefficient of variation (CV)0.33775921
Kurtosis-0.068039615
Mean1.3872
Median Absolute Deviation (MAD)0.3
Skewness0.58711356
Sum34.68
Variance0.21952933
MonotonicityNot monotonic
2023-12-12T18:13:17.473748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.15 5
20.0%
1.5 2
 
8.0%
1.65 2
 
8.0%
1.05 2
 
8.0%
2.15 2
 
8.0%
0.95 2
 
8.0%
1.6 1
 
4.0%
0.85 1
 
4.0%
1.55 1
 
4.0%
1.25 1
 
4.0%
Other values (6) 6
24.0%
ValueCountFrequency (%)
0.5 1
 
4.0%
0.85 1
 
4.0%
0.95 2
 
8.0%
1.05 2
 
8.0%
1.06 1
 
4.0%
1.15 5
20.0%
1.25 1
 
4.0%
1.26 1
 
4.0%
1.5 2
 
8.0%
1.55 1
 
4.0%
ValueCountFrequency (%)
2.4 1
4.0%
2.2 1
4.0%
2.15 2
8.0%
1.66 1
4.0%
1.65 2
8.0%
1.6 1
4.0%
1.55 1
4.0%
1.5 2
8.0%
1.26 1
4.0%
1.25 1
4.0%

설치연도(준공)
Date

Distinct18
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum1910-01-01 00:00:00
Maximum1993-01-01 00:00:00
2023-12-12T18:13:17.637227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:17.825210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

국가 편입년도
Date

Distinct6
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum1966-01-01 00:00:00
Maximum1992-01-01 00:00:00
2023-12-12T18:13:17.978742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:18.115290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

Interactions

2023-12-12T18:13:10.960577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:04.968889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.721484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.538546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.390862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.295386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.293230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.162861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:11.064779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.045616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.825027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.642595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.492147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.397159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.390107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.261103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:11.171131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.131575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.917672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.737167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.613297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.535432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.507825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.370761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:11.265835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.221392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.995785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.856786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.721194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.647921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.620410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.469840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:11.383873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.314594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.095711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.969898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.844642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.781841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.737017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.564571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:11.491554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.409131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.206133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.090542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.953204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.913162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.859726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.659966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:11.623904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.508134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.336347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.198134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.074755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.034142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.964849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.756302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:12.039427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:05.616337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:06.438227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:07.294056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:08.173850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:09.159073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.063879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:10.854891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:13:18.235863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리구분관리자명지구명읍면면적 -구역 (ha)면적- 몽리 (ha)방조제- 조(m)방조제- 연장(m)배수갑문(개소)배수갑문(연수)포 용 조수량(천m3)제 고- 최고(m)제고 - 평균(m)여유고 (m)설치연도(준공)국가 편입년도
관리구분1.0000.5541.0000.4600.7740.7900.2380.7430.0000.7560.7190.0730.3590.7850.9621.000
관리자명0.5541.0001.0000.0830.9000.7220.0000.6830.0000.5850.8710.2160.0000.5180.6341.000
지구명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
읍면0.4600.0831.0001.0000.0000.0000.0000.0000.0000.0000.2890.0000.6910.0000.8380.600
면적 -구역 (ha)0.7740.9001.0000.0001.0000.9080.8420.8930.4750.9150.9310.2900.3130.6890.8990.757
면적- 몽리 (ha)0.7900.7221.0000.0000.9081.0001.0000.9880.5950.9850.8130.5640.6170.6620.8100.745
방조제- 조(m)0.2380.0001.0000.0000.8421.0001.0001.0000.3820.3410.0001.0001.0001.0000.0000.786
방조제- 연장(m)0.7430.6831.0000.0000.8930.9881.0001.0000.5950.9650.5450.5240.7840.5550.6570.620
배수갑문(개소)0.0000.0001.0000.0000.4750.5950.3820.5951.0000.2360.0850.6320.7230.6750.0000.625
배수갑문(연수)0.7560.5851.0000.0000.9150.9850.3410.9650.2361.0000.8140.3730.0000.2460.9550.655
포 용 조수량(천m3)0.7190.8711.0000.2890.9310.8130.0000.5450.0850.8141.0000.0000.0000.6281.0000.891
제 고- 최고(m)0.0730.2161.0000.0000.2900.5641.0000.5240.6320.3730.0001.0000.6940.6680.0000.393
제고 - 평균(m)0.3590.0001.0000.6910.3130.6171.0000.7840.7230.0000.0000.6941.0000.6430.5020.633
여유고 (m)0.7850.5181.0000.0000.6890.6621.0000.5550.6750.2460.6280.6680.6431.0000.4710.577
설치연도(준공)0.9620.6341.0000.8380.8990.8100.0000.6570.0000.9551.0000.0000.5020.4711.0000.910
국가 편입년도1.0001.0001.0000.6000.7570.7450.7860.6200.6250.6550.8910.3930.6330.5770.9101.000
2023-12-12T18:13:18.455983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리구분배수갑문(개소)읍면방조제- 조(m)관리자명
관리구분1.0000.0000.2990.3760.804
배수갑문(개소)0.0001.0000.0000.2460.000
읍면0.2990.0001.0000.0000.000
방조제- 조(m)0.3760.2460.0001.0000.000
관리자명0.8040.0000.0000.0001.000
2023-12-12T18:13:18.627782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적 -구역 (ha)면적- 몽리 (ha)방조제- 연장(m)배수갑문(연수)포 용 조수량(천m3)제 고- 최고(m)제고 - 평균(m)여유고 (m)관리구분관리자명읍면방조제- 조(m)배수갑문(개소)
면적 -구역 (ha)1.0000.8690.5270.8950.7600.5020.1150.3490.8100.6800.0000.6080.299
면적- 몽리 (ha)0.8691.0000.6060.8770.9060.467-0.0310.2050.7670.7980.0000.9330.667
방조제- 연장(m)0.5270.6061.0000.6230.3980.4150.2330.2470.7050.7590.0000.9330.667
배수갑문(연수)0.8950.8770.6231.0000.7530.4780.1190.3630.7210.6550.0000.3810.257
포 용 조수량(천m3)0.7600.9060.3980.7531.0000.353-0.1670.1000.7410.6420.1550.0000.000
제 고- 최고(m)0.5020.4670.4150.4780.3531.0000.5930.3510.0000.1790.0000.8850.598
제고 - 평균(m)0.115-0.0310.2330.119-0.1670.5931.0000.5220.1450.0000.3830.8080.446
여유고 (m)0.3490.2050.2470.3630.1000.3510.5221.0000.4580.4710.0000.8600.601
관리구분0.8100.7670.7050.7210.7410.0000.1450.4581.0000.8040.2990.3760.000
관리자명0.6800.7980.7590.6550.6420.1790.0000.4710.8041.0000.0000.0000.000
읍면0.0000.0000.0000.0000.1550.0000.3830.0000.2990.0001.0000.0000.000
방조제- 조(m)0.6080.9330.9330.3810.0000.8850.8080.8600.3760.0000.0001.0000.246
배수갑문(개소)0.2990.6670.6670.2570.0000.5980.4460.6010.0000.0000.0000.2461.000

Missing values

2023-12-12T18:13:12.239909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:13:12.494604image/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

시군관리구분관리자명지구명시읍면면적 -구역 (ha)면적- 몽리 (ha)방조제- 조(m)방조제- 연장(m)배수갑문(개소)배수갑문(연수)포 용 조수량(천m3)제 고- 최고(m)제고 - 평균(m)여유고 (m)설치연도(준공)국가 편입년도
0여수시국가관리순천지사관기전라남도 여수시소라301.0188.0149711258013.63.51.61922-01-011966-01-01
1여수시국가관리순천지사대포전라남도 여수시소라425.0280.01215011859465.14.52.21925-01-011966-01-01
2여수시도관리여수시장옥적전라남도 여수시화양55.052.012701618185.02.70.51931-01-011985-01-01
3여수시도관리여수시장묘도전라남도 여수시묘도8.58.51257121125.25.21.51945-01-011985-01-01
4여수시도관리순천지사조화전라남도 여수시율촌60.258.221338262775.65.62.41940-01-011981-01-01
5여수시도관리순천지사취적전라남도 여수시율촌35.032.01238149304.03.81.51942-01-011981-01-01
6여수시시관리여수시장화태전라남도 여수시남9.09.01120111983.53.41.051968-01-011991-01-01
7여수시시관리여수시장죽포전라남도 여수시돌산38.038.0170149523.61.91.061959-01-011990-01-01
8여수시시관리여수시장달천전라남도 여수시소라20.020.01225125364.02.31.261910-01-011990-01-01
9여수시시관리여수시장대곡전라남도 여수시소라39.039.012892410584.03.11.661920-01-011990-01-01
시군관리구분관리자명지구명시읍면면적 -구역 (ha)면적- 몽리 (ha)방조제- 조(m)방조제- 연장(m)배수갑문(개소)배수갑문(연수)포 용 조수량(천m3)제 고- 최고(m)제고 - 평균(m)여유고 (m)설치연도(준공)국가 편입년도
15여수시시관리여수시장가포전라남도 여수시화양14.04.41160111045.04.01.151975-01-011991-01-01
16여수시시관리여수시장마상전라남도 여수시화양16.116.11203124225.06.52.151984-01-011991-01-01
17여수시시관리여수시장석교전라남도 여수시화양10.010.01300111983.03.20.951940-01-011991-01-01
18여수시시관리여수시장세포전라남도 여수시화양26.04.01160121744.54.61.251968-01-011991-01-01
19여수시시관리여수시장안정전라남도 여수시화양18.018.01250153172.43.11.151932-01-011991-01-01
20여수시시관리여수시장오천전라남도 여수시화양1.01.0116011303.03.01.651955-01-011991-01-01
21여수시시관리여수시장용주전라남도 여수시화양13.013.01316122914.54.21.151969-01-011991-01-01
22여수시시관리여수시장운두전라남도 여수시화양8.01.418011993.03.21.551960-01-011991-01-01
23여수시시관리여수시장제도전라남도 여수시화정10.02.0113811352.84.01.651936-01-011991-01-01
24여수시시관리여수시장개도전라남도 여수시화정39.013.7144114685.05.02.151993-01-011991-01-01