Overview

Dataset statistics

Number of variables7
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory61.8 B

Variable types

Categorical2
Text3
Numeric2

Dataset

Description경상북도 내수면 낚시터업 등록 현황자료로, 낚시터 관할 시군, 명칭, 주소, 수면적, 등록자, 이용금액 등의 항목이 있습니다.
Author경상북도
URLhttps://www.data.go.kr/data/15044794/fileData.do

Alerts

사업장명칭(낚시터명칭) has unique valuesUnique
위치 has unique valuesUnique
등록자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:10:41.042618
Analysis finished2023-12-12 01:10:42.208040
Duration1.17 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

Distinct15
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
김천시
6 
안동시
3 
구미시
3 
영주시
3 
상주시
3 
Other values (10)
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)14.3%

Sample

1st row포항시
2nd row포항시
3rd row경주시
4th row영천시
5th row김천시

Common Values

ValueCountFrequency (%)
김천시 6
17.1%
안동시 3
8.6%
구미시 3
8.6%
영주시 3
8.6%
상주시 3
8.6%
문경시 3
8.6%
칠곡군 3
8.6%
포항시 2
 
5.7%
경산시 2
 
5.7%
고령군 2
 
5.7%
Other values (5) 5
14.3%

Length

2023-12-12T10:10:42.310311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김천시 6
17.1%
안동시 3
8.6%
구미시 3
8.6%
영주시 3
8.6%
상주시 3
8.6%
문경시 3
8.6%
칠곡군 3
8.6%
포항시 2
 
5.7%
경산시 2
 
5.7%
고령군 2
 
5.7%
Other values (5) 5
14.3%

사업장명칭(낚시터명칭)
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T10:10:42.609345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.7428571
Min length2

Characters and Unicode

Total characters201
Distinct characters78
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row대물낚시터
2nd row사계절낚시터
3rd row광지낚시터
4th row삼사낚시터
5th row아포사계절낚시터
ValueCountFrequency (%)
대물낚시터 1
 
2.8%
조마낚시터 1
 
2.8%
암반수낚시터 1
 
2.8%
이천낚시터 1
 
2.8%
세천낚시터 1
 
2.8%
우지메기낚시터 1
 
2.8%
돌다리낚시터 1
 
2.8%
역돔낚시터 1
 
2.8%
평사 1
 
2.8%
평은낚시터 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T10:10:43.077823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 32
 
15.9%
낚 31
 
15.4%
터 28
 
13.9%
대 4
 
2.0%
사 4
 
2.0%
내 4
 
2.0%
실 3
 
1.5%
기 3
 
1.5%
원 3
 
1.5%
포 3
 
1.5%
Other values (68) 86
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 198
98.5%
Uppercase Letter 2
 
1.0%
Space Separator 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 32
 
16.2%
낚 31
 
15.7%
터 28
 
14.1%
대 4
 
2.0%
사 4
 
2.0%
내 4
 
2.0%
실 3
 
1.5%
기 3
 
1.5%
원 3
 
1.5%
포 3
 
1.5%
Other values (65) 83
41.9%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 198
98.5%
Latin 2
 
1.0%
Common 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 32
 
16.2%
낚 31
 
15.7%
터 28
 
14.1%
대 4
 
2.0%
사 4
 
2.0%
내 4
 
2.0%
실 3
 
1.5%
기 3
 
1.5%
원 3
 
1.5%
포 3
 
1.5%
Other values (65) 83
41.9%
Latin
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 198
98.5%
ASCII 3
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 32
 
16.2%
낚 31
 
15.7%
터 28
 
14.1%
대 4
 
2.0%
사 4
 
2.0%
내 4
 
2.0%
실 3
 
1.5%
기 3
 
1.5%
원 3
 
1.5%
포 3
 
1.5%
Other values (65) 83
41.9%
ASCII
ValueCountFrequency (%)
C 1
33.3%
I 1
33.3%
1
33.3%

위치
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T10:10:43.464344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length23
Mean length17.6
Min length11

Characters and Unicode

Total characters616
Distinct characters111
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

Unique35 ?
Unique (%)100.0%

Sample

1st row포항시 북구 기계면 봉계리 528번지 외 3필지(527, 1323, 1321)
2nd row포항시 북구 기계면 성계리 488-1
3rd row경주시 건천읍 조전리 514-1번지
4th row영천시 고경면 용전리 891
5th row김천시 아포읍 제석리 1431-7
ValueCountFrequency (%)
김천시 6
 
4.2%
영주시 3
 
2.1%
칠곡군 3
 
2.1%
상주시 3
 
2.1%
구미시 3
 
2.1%
문경시 3
 
2.1%
안동시 3
 
2.1%
아포읍 3
 
2.1%
북구 2
 
1.4%
외서면 2
 
1.4%
Other values (107) 111
78.2%
2023-12-12T10:10:44.020000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
 
17.4%
2 28
 
4.5%
시 27
 
4.4%
1 25
 
4.1%
- 23
 
3.7%
리 22
 
3.6%
3 20
 
3.2%
면 20
 
3.2%
5 16
 
2.6%
4 16
 
2.6%
Other values (101) 312
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 318
51.6%
Decimal Number 155
25.2%
Space Separator 107
 
17.4%
Dash Punctuation 23
 
3.7%
Other Punctuation 7
 
1.1%
Close Punctuation 3
 
0.5%
Open Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 27
 
8.5%
리 22
 
6.9%
면 20
 
6.3%
천 11
 
3.5%
동 10
 
3.1%
경 9
 
2.8%
읍 9
 
2.8%
주 8
 
2.5%
군 8
 
2.5%
김 6
 
1.9%
Other values (86) 188
59.1%
Decimal Number
ValueCountFrequency (%)
2 28
18.1%
1 25
16.1%
3 20
12.9%
5 16
10.3%
4 16
10.3%
7 13
8.4%
8 10
 
6.5%
6 10
 
6.5%
9 9
 
5.8%
0 8
 
5.2%
Space Separator
ValueCountFrequency (%)
107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 318
51.6%
Common 298
48.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 27
 
8.5%
리 22
 
6.9%
면 20
 
6.3%
천 11
 
3.5%
동 10
 
3.1%
경 9
 
2.8%
읍 9
 
2.8%
주 8
 
2.5%
군 8
 
2.5%
김 6
 
1.9%
Other values (86) 188
59.1%
Common
ValueCountFrequency (%)
107
35.9%
2 28
 
9.4%
1 25
 
8.4%
- 23
 
7.7%
3 20
 
6.7%
5 16
 
5.4%
4 16
 
5.4%
7 13
 
4.4%
8 10
 
3.4%
6 10
 
3.4%
Other values (5) 30
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 318
51.6%
ASCII 298
48.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
107
35.9%
2 28
 
9.4%
1 25
 
8.4%
- 23
 
7.7%
3 20
 
6.7%
5 16
 
5.4%
4 16
 
5.4%
7 13
 
4.4%
8 10
 
3.4%
6 10
 
3.4%
Other values (5) 30
 
10.1%
Hangul
ValueCountFrequency (%)
시 27
 
8.5%
리 22
 
6.9%
면 20
 
6.3%
천 11
 
3.5%
동 10
 
3.1%
경 9
 
2.8%
읍 9
 
2.8%
주 8
 
2.5%
군 8
 
2.5%
김 6
 
1.9%
Other values (86) 188
59.1%

적용수면
Categorical

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
5호
23 
실내
7 
카페
3 
5호,실내
 
2

Length

Max length5
Median length2
Mean length2.1714286
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5호
2nd row5호
3rd row5호
4th row5호
5th row5호

Common Values

ValueCountFrequency (%)
5호 23
65.7%
실내 7
 
20.0%
카페 3
 
8.6%
5호,실내 2
 
5.7%

Length

2023-12-12T10:10:44.198059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:10:44.313066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5호 23
65.7%
실내 7
 
20.0%
카페 3
 
8.6%
5호,실내 2
 
5.7%

수면적(ha)
Real number (ℝ)

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35118857
Minimum0.0035
Maximum2.159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T10:10:44.436766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0035
5-th percentile0.00675
Q10.0496
median0.13
Q30.41125
95-th percentile1.2067
Maximum2.159
Range2.1555
Interquartile range (IQR)0.36165

Descriptive statistics

Standard deviation0.4869786
Coefficient of variation (CV)1.3866584
Kurtosis4.9998442
Mean0.35118857
Median Absolute Deviation (MAD)0.1045
Skewness2.16727
Sum12.2916
Variance0.23714816
MonotonicityNot monotonic
2023-12-12T10:10:44.585662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.13 2
 
5.7%
0.03 2
 
5.7%
0.523 1
 
2.9%
0.05 1
 
2.9%
0.2345 1
 
2.9%
2.159 1
 
2.9%
1.5 1
 
2.9%
1.04 1
 
2.9%
0.22 1
 
2.9%
0.16 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
0.0035 1
2.9%
0.005 1
2.9%
0.0075 1
2.9%
0.0078 1
2.9%
0.0167 1
2.9%
0.03 2
5.7%
0.0448 1
2.9%
0.0492 1
2.9%
0.05 1
2.9%
0.07 1
2.9%
ValueCountFrequency (%)
2.159 1
2.9%
1.5 1
2.9%
1.081 1
2.9%
1.04 1
2.9%
0.9 1
2.9%
0.8804 1
2.9%
0.7412 1
2.9%
0.523 1
2.9%
0.4625 1
2.9%
0.36 1
2.9%

등록자
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T10:10:44.828228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105
Distinct characters67
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

Unique35 ?
Unique (%)100.0%

Sample

1st row김말순
2nd row고정숙
3rd row도명조
4th row심정철
5th row황전석
ValueCountFrequency (%)
김말순 1
 
2.9%
심삼보 1
 
2.9%
박재열 1
 
2.9%
김학구 1
 
2.9%
김태훈 1
 
2.9%
이영무 1
 
2.9%
박운화 1
 
2.9%
홍종희 1
 
2.9%
김응길 1
 
2.9%
김종율 1
 
2.9%
Other values (25) 25
71.4%
2023-12-12T10:10:45.262987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 10
 
9.5%
박 4
 
3.8%
금 3
 
2.9%
정 3
 
2.9%
강 3
 
2.9%
숙 3
 
2.9%
환 2
 
1.9%
종 2
 
1.9%
진 2
 
1.9%
이 2
 
1.9%
Other values (57) 71
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 10
 
9.5%
박 4
 
3.8%
금 3
 
2.9%
정 3
 
2.9%
강 3
 
2.9%
숙 3
 
2.9%
환 2
 
1.9%
종 2
 
1.9%
진 2
 
1.9%
이 2
 
1.9%
Other values (57) 71
67.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 105
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 10
 
9.5%
박 4
 
3.8%
금 3
 
2.9%
정 3
 
2.9%
강 3
 
2.9%
숙 3
 
2.9%
환 2
 
1.9%
종 2
 
1.9%
진 2
 
1.9%
이 2
 
1.9%
Other values (57) 71
67.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 105
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 10
 
9.5%
박 4
 
3.8%
금 3
 
2.9%
정 3
 
2.9%
강 3
 
2.9%
숙 3
 
2.9%
환 2
 
1.9%
종 2
 
1.9%
진 2
 
1.9%
이 2
 
1.9%
Other values (57) 71
67.6%

이용료(원_1일)
Real number (ℝ)

Distinct8
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17971.429
Minimum5000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T10:10:45.393486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile9700
Q110000
median20000
Q325000
95-th percentile30000
Maximum40000
Range35000
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation8272.8675
Coefficient of variation (CV)0.46033444
Kurtosis-0.20434294
Mean17971.429
Median Absolute Deviation (MAD)10000
Skewness0.59901076
Sum629000
Variance68440336
MonotonicityNot monotonic
2023-12-12T10:10:45.548954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10000 11
31.4%
20000 8
22.9%
25000 5
14.3%
15000 4
 
11.4%
30000 4
 
11.4%
9000 1
 
2.9%
40000 1
 
2.9%
5000 1
 
2.9%
ValueCountFrequency (%)
5000 1
 
2.9%
9000 1
 
2.9%
10000 11
31.4%
15000 4
 
11.4%
20000 8
22.9%
25000 5
14.3%
30000 4
 
11.4%
40000 1
 
2.9%
ValueCountFrequency (%)
40000 1
 
2.9%
30000 4
 
11.4%
25000 5
14.3%
20000 8
22.9%
15000 4
 
11.4%
10000 11
31.4%
9000 1
 
2.9%
5000 1
 
2.9%

Interactions

2023-12-12T10:10:41.656154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:41.435180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:41.766994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:41.526333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:10:45.965623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구사업장명칭(낚시터명칭)위치적용수면수면적(ha)등록자이용료(원_1일)
시군구1.0001.0001.0000.8220.7501.0000.000
사업장명칭(낚시터명칭)1.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.000
적용수면0.8221.0001.0001.0000.0001.0000.000
수면적(ha)0.7501.0001.0000.0001.0001.0000.000
등록자1.0001.0001.0001.0001.0001.0001.000
이용료(원_1일)0.0001.0001.0000.0000.0001.0001.000
2023-12-12T10:10:46.137068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구적용수면
시군구1.0000.500
적용수면0.5001.000
2023-12-12T10:10:46.231507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수면적(ha)이용료(원_1일)시군구적용수면
수면적(ha)1.0000.1080.3770.000
이용료(원_1일)0.1081.0000.0000.000
시군구0.3770.0001.0000.500
적용수면0.0000.0000.5001.000

Missing values

2023-12-12T10:10:41.968248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:10:42.147159image/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

시군구사업장명칭(낚시터명칭)위치적용수면수면적(ha)등록자이용료(원_1일)
0포항시대물낚시터포항시 북구 기계면 봉계리 528번지 외 3필지(527, 1323, 1321)5호0.523김말순9000
1포항시사계절낚시터포항시 북구 기계면 성계리 488-15호0.4625고정숙10000
2경주시광지낚시터경주시 건천읍 조전리 514-1번지5호1.081도명조25000
3영천시삼사낚시터영천시 고경면 용전리 8915호0.08심정철10000
4김천시아포사계절낚시터김천시 아포읍 제석리 1431-75호0.31황전석40000
5김천시아포과수원낚시터김천시 아포읍 국사리 155-15호0.13김형수10000
6김천시배시네낚시터김천시 아포읍 의리 121-15호0.13김인직20000
7김천시복강유료낚시터김천시 대항면 복전리 188-25호0.15전덕환25000
8김천시봉곡낚시터김천시 농소면 봉곡리 67-35호0.03강석진15000
9김천시조마낚시터김천시 조마면 강곡리 622-15호0.078박희상10000
시군구사업장명칭(낚시터명칭)위치적용수면수면적(ha)등록자이용료(원_1일)
25경산시평사경산시 진량읍 평사리 42-2실내0.22홍종희10000
26경산시두메경산시 남천면 신방리 602, 603-1, 604-1, 604-25호0.16김종율30000
27의성군암반수낚시터의성군 다인면 덕지리 125-25호0.05금성원30000
28청도군매전낚시터청도군 매전면 호화리 752-715호0.36최시진15000
29고령군다산나정고령군 다산면 312-27실내0.07은무기10000
30고령군대가야고령군 대가야읍 정정골길 67-39실내0.125박유순20000
31성주군명포낚시터성주군 선남면 명포리 155호0.9황금례10000
32칠곡군왜관IC실내낚시터칠곡군 지천면 연화리 433-3실내0.0078임월선15000
33칠곡군봇또랑낚시터칠곡군 북삼읍 549-5실내0.03이경자25000
34칠곡군강태공낚시터칠곡군 왜관읍 석전리 455-3,455-6실내0.0492강성환20000