Overview

Dataset statistics

Number of variables9
Number of observations29
Missing cells7
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory78.4 B

Variable types

Numeric2
Categorical3
Text4

Dataset

Description전북특별자치도 시군별 낚시터 및 유어장 등록 현황에 관한 데이터입니다. (시군별, 상호명, 소유자, 위치, 수면적, 유효기간 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055798/fileData.do

Alerts

구분 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
비 고 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
시군구 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
소유자 has 7 (24.1%) missing valuesMissing
순번 has unique valuesUnique
명칭 has unique valuesUnique
유효기간 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:12:50.221913
Analysis finished2024-03-15 01:12:52.332968
Duration2.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T10:12:52.508665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q18
median15
Q322
95-th percentile27.6
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5146932
Coefficient of variation (CV)0.56764621
Kurtosis-1.2
Mean15
Median Absolute Deviation (MAD)7
Skewness0
Sum435
Variance72.5
MonotonicityStrictly increasing
2024-03-15T10:12:52.823377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 1
 
3.4%
2 1
 
3.4%
29 1
 
3.4%
28 1
 
3.4%
27 1
 
3.4%
26 1
 
3.4%
25 1
 
3.4%
24 1
 
3.4%
23 1
 
3.4%
22 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1 1
3.4%
2 1
3.4%
3 1
3.4%
4 1
3.4%
5 1
3.4%
6 1
3.4%
7 1
3.4%
8 1
3.4%
9 1
3.4%
10 1
3.4%
ValueCountFrequency (%)
29 1
3.4%
28 1
3.4%
27 1
3.4%
26 1
3.4%
25 1
3.4%
24 1
3.4%
23 1
3.4%
22 1
3.4%
21 1
3.4%
20 1
3.4%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size360.0 B
낚시터
23 
유어장
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
낚시터 23
79.3%
유어장 6
 
20.7%

Length

2024-03-15T10:12:53.062897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:12:53.386653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낚시터 23
79.3%
유어장 6
 
20.7%

시군구
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size360.0 B
익산시
8 
군산시
5 
완주군
3 
부안군
3 
전주시
2 
Other values (5)
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)6.9%

Sample

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

Common Values

ValueCountFrequency (%)
익산시 8
27.6%
군산시 5
17.2%
완주군 3
 
10.3%
부안군 3
 
10.3%
전주시 2
 
6.9%
정읍시 2
 
6.9%
김제시 2
 
6.9%
임실군 2
 
6.9%
남원시 1
 
3.4%
진안군 1
 
3.4%

Length

2024-03-15T10:12:53.728433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:12:54.034512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
익산시 8
27.6%
군산시 5
17.2%
완주군 3
 
10.3%
부안군 3
 
10.3%
전주시 2
 
6.9%
정읍시 2
 
6.9%
김제시 2
 
6.9%
임실군 2
 
6.9%
남원시 1
 
3.4%
진안군 1
 
3.4%

명칭
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size360.0 B
2024-03-15T10:12:54.917105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.3103448
Min length2

Characters and Unicode

Total characters183
Distinct characters78
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row자바라실내낚시터
2nd row뻐끔실내낚시터
3rd row신시도유료낚시터
4th row낚Go먹GO
5th row옛골낚시터
ValueCountFrequency (%)
선유 2
 
5.4%
체험장 2
 
5.4%
갯벌체험 2
 
5.4%
사람들 1
 
2.7%
우리낚시터 1
 
2.7%
백화양어장 1
 
2.7%
호암낚시터 1
 
2.7%
자연을 1
 
2.7%
닮은 1
 
2.7%
자바라실내낚시터 1
 
2.7%
Other values (24) 24
64.9%
2024-03-15T10:12:55.934577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
낚 19
 
10.4%
시 19
 
10.4%
터 19
 
10.4%
8
 
4.4%
체 5
 
2.7%
갯 5
 
2.7%
벌 5
 
2.7%
험 5
 
2.7%
장 4
 
2.2%
우 3
 
1.6%
Other values (68) 91
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 169
92.3%
Space Separator 8
 
4.4%
Uppercase Letter 3
 
1.6%
Decimal Number 2
 
1.1%
Lowercase Letter 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
낚 19
 
11.2%
시 19
 
11.2%
터 19
 
11.2%
체 5
 
3.0%
갯 5
 
3.0%
벌 5
 
3.0%
험 5
 
3.0%
장 4
 
2.4%
우 3
 
1.8%
스 3
 
1.8%
Other values (62) 82
48.5%
Uppercase Letter
ValueCountFrequency (%)
G 2
66.7%
O 1
33.3%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 169
92.3%
Common 10
 
5.5%
Latin 4
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
낚 19
 
11.2%
시 19
 
11.2%
터 19
 
11.2%
체 5
 
3.0%
갯 5
 
3.0%
벌 5
 
3.0%
험 5
 
3.0%
장 4
 
2.4%
우 3
 
1.8%
스 3
 
1.8%
Other values (62) 82
48.5%
Common
ValueCountFrequency (%)
8
80.0%
3 1
 
10.0%
2 1
 
10.0%
Latin
ValueCountFrequency (%)
G 2
50.0%
o 1
25.0%
O 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 169
92.3%
ASCII 14
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
낚 19
 
11.2%
시 19
 
11.2%
터 19
 
11.2%
체 5
 
3.0%
갯 5
 
3.0%
벌 5
 
3.0%
험 5
 
3.0%
장 4
 
2.4%
우 3
 
1.8%
스 3
 
1.8%
Other values (62) 82
48.5%
ASCII
ValueCountFrequency (%)
8
57.1%
G 2
 
14.3%
3 1
 
7.1%
2 1
 
7.1%
o 1
 
7.1%
O 1
 
7.1%

소유자
Text

MISSING 

Distinct15
Distinct (%)68.2%
Missing7
Missing (%)24.1%
Memory size360.0 B
2024-03-15T10:12:56.467256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters66
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)50.0%

Sample

1st row박**
2nd row이**
3rd row강**
4th row정**
5th row김**
ValueCountFrequency (%)
김 4
18.2%
박 3
13.6%
이 2
 
9.1%
강 2
 
9.1%
정 1
 
4.5%
문 1
 
4.5%
최 1
 
4.5%
조 1
 
4.5%
채 1
 
4.5%
유 1
 
4.5%
Other values (5) 5
22.7%
2024-03-15T10:12:57.279139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 40
60.6%
김 4
 
6.1%
박 3
 
4.5%
이 2
 
3.0%
강 2
 
3.0%
화 1
 
1.5%
진 1
 
1.5%
황 1
 
1.5%
송 1
 
1.5%
계 1
 
1.5%
Other values (10) 10
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 40
60.6%
Other Letter 26
39.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 4
15.4%
박 3
 
11.5%
이 2
 
7.7%
강 2
 
7.7%
화 1
 
3.8%
진 1
 
3.8%
황 1
 
3.8%
송 1
 
3.8%
계 1
 
3.8%
업 1
 
3.8%
Other values (9) 9
34.6%
Other Punctuation
ValueCountFrequency (%)
* 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40
60.6%
Hangul 26
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 4
15.4%
박 3
 
11.5%
이 2
 
7.7%
강 2
 
7.7%
화 1
 
3.8%
진 1
 
3.8%
황 1
 
3.8%
송 1
 
3.8%
계 1
 
3.8%
업 1
 
3.8%
Other values (9) 9
34.6%
Common
ValueCountFrequency (%)
* 40
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40
60.6%
Hangul 26
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 40
100.0%
Hangul
ValueCountFrequency (%)
김 4
15.4%
박 3
 
11.5%
이 2
 
7.7%
강 2
 
7.7%
화 1
 
3.8%
진 1
 
3.8%
황 1
 
3.8%
송 1
 
3.8%
계 1
 
3.8%
업 1
 
3.8%
Other values (9) 9
34.6%

주 소
Text

Distinct27
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size360.0 B
2024-03-15T10:12:58.122833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length11.137931
Min length3

Characters and Unicode

Total characters323
Distinct characters79
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

Unique25 ?
Unique (%)86.2%

Sample

1st row완산구 평화로 69(지하1)
2nd row완산구 전라감영5길 27
3rd row옥도면 신시도 지선
4th row수송로 17(지하1)
5th row춘표면 신동리 655
ValueCountFrequency (%)
변산면 3
 
3.8%
고산면 3
 
3.8%
선유도 2
 
2.6%
완산구 2
 
2.6%
도청리지선 2
 
2.6%
왕궁면 2
 
2.6%
신시도 2
 
2.6%
양야리 2
 
2.6%
모리생길 1
 
1.3%
남봉리 1
 
1.3%
Other values (58) 58
74.4%
2024-03-15T10:12:59.402158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
15.2%
면 20
 
6.2%
리 18
 
5.6%
1 17
 
5.3%
산 12
 
3.7%
7 11
 
3.4%
2 11
 
3.4%
5 11
 
3.4%
- 11
 
3.4%
3 9
 
2.8%
Other values (69) 154
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 173
53.6%
Decimal Number 84
26.0%
Space Separator 49
 
15.2%
Dash Punctuation 11
 
3.4%
Other Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%
Open Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 20
 
11.6%
리 18
 
10.4%
산 12
 
6.9%
지 7
 
4.0%
도 7
 
4.0%
선 6
 
3.5%
로 5
 
2.9%
신 5
 
2.9%
용 4
 
2.3%
평 4
 
2.3%
Other values (54) 85
49.1%
Decimal Number
ValueCountFrequency (%)
1 17
20.2%
7 11
13.1%
2 11
13.1%
5 11
13.1%
3 9
10.7%
6 9
10.7%
0 6
 
7.1%
4 4
 
4.8%
8 3
 
3.6%
9 3
 
3.6%
Space Separator
ValueCountFrequency (%)
49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 173
53.6%
Common 150
46.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 20
 
11.6%
리 18
 
10.4%
산 12
 
6.9%
지 7
 
4.0%
도 7
 
4.0%
선 6
 
3.5%
로 5
 
2.9%
신 5
 
2.9%
용 4
 
2.3%
평 4
 
2.3%
Other values (54) 85
49.1%
Common
ValueCountFrequency (%)
49
32.7%
1 17
 
11.3%
7 11
 
7.3%
2 11
 
7.3%
5 11
 
7.3%
- 11
 
7.3%
3 9
 
6.0%
6 9
 
6.0%
0 6
 
4.0%
4 4
 
2.7%
Other values (5) 12
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 173
53.6%
ASCII 150
46.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
32.7%
1 17
 
11.3%
7 11
 
7.3%
2 11
 
7.3%
5 11
 
7.3%
- 11
 
7.3%
3 9
 
6.0%
6 9
 
6.0%
0 6
 
4.0%
4 4
 
2.7%
Other values (5) 12
 
8.0%
Hangul
ValueCountFrequency (%)
면 20
 
11.6%
리 18
 
10.4%
산 12
 
6.9%
지 7
 
4.0%
도 7
 
4.0%
선 6
 
3.5%
로 5
 
2.9%
신 5
 
2.9%
용 4
 
2.3%
평 4
 
2.3%
Other values (54) 85
49.1%

수면적
Real number (ℝ)

Distinct27
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13751.775
Minimum4.2
Maximum339010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T10:12:59.759629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile4.88
Q160
median700
Q32999
95-th percentile11265.2
Maximum339010
Range339005.8
Interquartile range (IQR)2939

Descriptive statistics

Standard deviation62636.715
Coefficient of variation (CV)4.5548094
Kurtosis28.834146
Mean13751.775
Median Absolute Deviation (MAD)690.01
Skewness5.3630937
Sum398801.48
Variance3.923358 × 109
MonotonicityNot monotonic
2024-03-15T10:12:59.982404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
9.99 2
 
6.9%
400.0 2
 
6.9%
330.0 1
 
3.4%
6704.0 1
 
3.4%
4.8 1
 
3.4%
12.5 1
 
3.4%
5.0 1
 
3.4%
4.2 1
 
3.4%
4800.0 1
 
3.4%
1200.0 1
 
3.4%
Other values (17) 17
58.6%
ValueCountFrequency (%)
4.2 1
3.4%
4.8 1
3.4%
5.0 1
3.4%
9.99 2
6.9%
12.5 1
3.4%
53.0 1
3.4%
60.0 1
3.4%
90.0 1
3.4%
214.0 1
3.4%
285.0 1
3.4%
ValueCountFrequency (%)
339010.0 1
3.4%
12100.0 1
3.4%
10013.0 1
3.4%
6704.0 1
3.4%
6400.0 1
3.4%
5570.0 1
3.4%
4800.0 1
3.4%
2999.0 1
3.4%
2227.0 1
3.4%
1900.0 1
3.4%

유효기간
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size360.0 B
2024-03-15T10:13:00.666563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters609
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row2016-11-10~2026-11-09
2nd row2017-05-25~2019-05-24
3rd row2019-12-16~2022-11-27
4th row2019-06-26~2022-09-23
5th row2016-07-15~2026-07-14
ValueCountFrequency (%)
2016-11-10~2026-11-09 1
 
3.4%
2013-03-14~2023-03-13 1
 
3.4%
2016-07-29~2020-01-27 1
 
3.4%
2014-08-20~2020-01-27 1
 
3.4%
2019-07-19~2020-06-14 1
 
3.4%
2019-07-19~2022-07-27 1
 
3.4%
2015-10-16~2020-09-16 1
 
3.4%
2015-03-16~2025-03-15 1
 
3.4%
2019-11-30~2021-11-17 1
 
3.4%
2019-06-14~2024-06-13 1
 
3.4%
Other values (19) 19
65.5%
2024-03-15T10:13:01.798879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 134
22.0%
- 116
19.0%
2 113
18.6%
1 91
14.9%
~ 29
 
4.8%
9 24
 
3.9%
5 24
 
3.9%
6 23
 
3.8%
3 19
 
3.1%
4 15
 
2.5%
Other values (2) 21
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 464
76.2%
Dash Punctuation 116
 
19.0%
Math Symbol 29
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134
28.9%
2 113
24.4%
1 91
19.6%
9 24
 
5.2%
5 24
 
5.2%
6 23
 
5.0%
3 19
 
4.1%
4 15
 
3.2%
7 13
 
2.8%
8 8
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 116
100.0%
Math Symbol
ValueCountFrequency (%)
~ 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134
22.0%
- 116
19.0%
2 113
18.6%
1 91
14.9%
~ 29
 
4.8%
9 24
 
3.9%
5 24
 
3.9%
6 23
 
3.8%
3 19
 
3.1%
4 15
 
2.5%
Other values (2) 21
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134
22.0%
- 116
19.0%
2 113
18.6%
1 91
14.9%
~ 29
 
4.8%
9 24
 
3.9%
5 24
 
3.9%
6 23
 
3.8%
3 19
 
3.1%
4 15
 
2.5%
Other values (2) 21
 
3.4%

비 고
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size360.0 B
등록
20 
허가
3 
마을어업
3 
마을어장
 
2
패류어장
 
1

Length

Max length4
Median length2
Mean length2.4137931
Min length2

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st row등록
2nd row등록
3rd row허가
4th row등록
5th row등록

Common Values

ValueCountFrequency (%)
등록 20
69.0%
허가 3
 
10.3%
마을어업 3
 
10.3%
마을어장 2
 
6.9%
패류어장 1
 
3.4%

Length

2024-03-15T10:13:02.115281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:13:02.316430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록 20
69.0%
허가 3
 
10.3%
마을어업 3
 
10.3%
마을어장 2
 
6.9%
패류어장 1
 
3.4%

Interactions

2024-03-15T10:12:51.118769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:50.811532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:51.342301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:50.959933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:13:02.489100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분시군구명칭소유자주 소수면적유효기간비 고
순번1.0001.0000.9801.0000.4291.0000.0001.0000.906
구분1.0001.0000.9421.000NaN1.0000.0001.0001.000
시군구0.9800.9421.0001.0000.0001.0000.0001.0000.866
명칭1.0001.0001.0001.0001.0001.0001.0001.0001.000
소유자0.429NaN0.0001.0001.0001.0001.0001.0001.000
주 소1.0001.0001.0001.0001.0001.0001.0001.0000.000
수면적0.0000.0000.0001.0001.0001.0001.0001.0000.366
유효기간1.0001.0001.0001.0001.0001.0001.0001.0001.000
비 고0.9061.0000.8661.0001.0000.0000.3661.0001.000
2024-03-15T10:13:02.809969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시군구비 고
구분1.0000.6650.943
시군구0.6651.0000.468
비 고0.9430.4681.000
2024-03-15T10:13:02.978652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번수면적구분시군구비 고
순번1.000-0.1230.8390.7490.526
수면적-0.1231.0000.0000.0000.416
구분0.8390.0001.0000.6650.943
시군구0.7490.0000.6651.0000.468
비 고0.5260.4160.9430.4681.000

Missing values

2024-03-15T10:12:51.716037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:12:52.165506image/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

순번구분시군구명칭소유자주 소수면적유효기간비 고
01낚시터전주시자바라실내낚시터박**완산구 평화로 69(지하1)330.02016-11-10~2026-11-09등록
12낚시터전주시뻐끔실내낚시터이**완산구 전라감영5길 27214.02017-05-25~2019-05-24등록
23낚시터군산시신시도유료낚시터<NA>옥도면 신시도 지선285.02019-12-16~2022-11-27허가
34낚시터군산시낚Go먹GO강**수송로 17(지하1)53.02019-06-26~2022-09-23등록
45낚시터익산시옛골낚시터정**춘표면 신동리 655400.02016-07-15~2026-07-14등록
56낚시터익산시팔봉낚시터김**춘포면 창평리 555400.02016-01-27~2026-01-26등록
67낚시터익산시왕궁낚시터김**왕궁면 호반로 231-251000.02019-01-21~2029-01-20등록
78낚시터익산시개울낚시터이**왕궁면 용화리 274-1900.02018-05-15~2028-03-31등록
89낚시터익산시두무낚시터박**용동면 대조리 두무 1길 28-41900.02015-03-11~2020-03-10등록
910낚시터익산시몬스터실내낚시카페문**신동 763-760.02018-05-16~2020-04-18등록
순번구분시군구명칭소유자주 소수면적유효기간비 고
1920낚시터완주군우리낚시터박**고산면 양야리 5072999.02014-09-22~2024-09-21등록
2021낚시터진안군백화양어장황**안천면 진무리 320610013.02019-06-14~2024-06-13허가
2122낚시터임실군호암낚시터진*신평면 호암리 174-11200.02019-11-30~2021-11-17등록
2223낚시터임실군자연을 닮은 사람들한**관촌면 용산리 176-64800.02015-03-16~2025-03-15등록
2324유어장군산시큰뻘<NA>신시도4.22015-10-16~2020-09-16마을어업
2425유어장군산시선유 2구 갯벌체험<NA>선유도5.02019-07-19~2022-07-27마을어업
2526유어장군산시선유 3구 갯벌체험<NA>선유도12.52019-07-19~2020-06-14마을어업
2627유어장부안군모항갯벌체험장<NA>변산면 도청리지선9.992014-08-20~2020-01-27패류어장
2728유어장부안군두포마을갯벌 체험장<NA>변산면 도청리지선4.82016-07-29~2020-01-27마을어장
2829유어장부안군대항갯벌 체험장<NA>변산면 대항리지선9.992019-05-31~2022-08-15마을어장