Overview

Dataset statistics

Number of variables4
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory880.0 B
Average record size in memory40.0 B

Variable types

Numeric2
Text2

Dataset

Description해양수산 관련하여 해양용도구역 최초 고시인 부산용도구역에 대한 데이터로 해양관광구역 정보를 파일 형태로 사용자는 확인 할 수 있다.
URLhttps://www.data.go.kr/data/15113949/fileData.do

Alerts

공간정보일련번호(gid) is highly overall correlated with 용도구역면적(ua_ar)High correlation
용도구역면적(ua_ar) is highly overall correlated with 공간정보일련번호(gid)High correlation
공간정보일련번호(gid) has unique valuesUnique
용도구역아이디(ua_id) has unique valuesUnique
용도구역면적(ua_ar) has 4 (18.2%) zerosZeros

Reproduction

Analysis started2023-12-12 19:46:43.258704
Analysis finished2023-12-12 19:46:44.012006
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공간정보일련번호(gid)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T04:46:44.084967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityNot monotonic
2023-12-13T04:46:44.264909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

용도구역아이디(ua_id)
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T04:46:44.511743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters462
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st rowTO351916N1291603E2020
2nd rowTO351908N1291552E2020
3rd rowTO351539N1291407E2020
4th rowTO350949N1291146E2020
5th rowTO351042N1291204E2020
ValueCountFrequency (%)
to351916n1291603e2020 1
 
4.5%
to351908n1291552e2020 1
 
4.5%
to350248n1285755e2020 1
 
4.5%
to350429n1290110e2020 1
 
4.5%
to345128n1284537e2020 1
 
4.5%
to345321n1290257e2020 1
 
4.5%
to345142n1290050e2020 1
 
4.5%
to345932n1284612e2020 1
 
4.5%
to350920n1290945e2020 1
 
4.5%
to350926n1290824e2020 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T04:46:44.936826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 87
18.8%
0 81
17.5%
1 55
11.9%
5 38
8.2%
3 33
 
7.1%
9 32
 
6.9%
T 22
 
4.8%
O 22
 
4.8%
N 22
 
4.8%
E 22
 
4.8%
Other values (4) 48
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 374
81.0%
Uppercase Letter 88
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 87
23.3%
0 81
21.7%
1 55
14.7%
5 38
10.2%
3 33
 
8.8%
9 32
 
8.6%
4 20
 
5.3%
8 13
 
3.5%
6 9
 
2.4%
7 6
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
T 22
25.0%
O 22
25.0%
N 22
25.0%
E 22
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 374
81.0%
Latin 88
 
19.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 87
23.3%
0 81
21.7%
1 55
14.7%
5 38
10.2%
3 33
 
8.8%
9 32
 
8.6%
4 20
 
5.3%
8 13
 
3.5%
6 9
 
2.4%
7 6
 
1.6%
Latin
ValueCountFrequency (%)
T 22
25.0%
O 22
25.0%
N 22
25.0%
E 22
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 87
18.8%
0 81
17.5%
1 55
11.9%
5 38
8.2%
3 33
 
7.1%
9 32
 
6.9%
T 22
 
4.8%
O 22
 
4.8%
N 22
 
4.8%
E 22
 
4.8%
Other values (4) 48
10.4%

용도구역면적(ua_ar)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7990909
Minimum0
Maximum26.21
Zeros4
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T04:46:45.145427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0225
median0.17
Q30.7675
95-th percentile14.4615
Maximum26.21
Range26.21
Interquartile range (IQR)0.745

Descriptive statistics

Standard deviation6.4297004
Coefficient of variation (CV)2.2970674
Kurtosis8.5372229
Mean2.7990909
Median Absolute Deviation (MAD)0.155
Skewness2.872993
Sum61.58
Variance41.341047
MonotonicityNot monotonic
2023-12-13T04:46:45.346792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 4
18.2%
0.01 1
 
4.5%
0.23 1
 
4.5%
14.7 1
 
4.5%
0.19 1
 
4.5%
0.24 1
 
4.5%
9.93 1
 
4.5%
0.93 1
 
4.5%
26.21 1
 
4.5%
5.57 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
0.0 4
18.2%
0.01 1
 
4.5%
0.02 1
 
4.5%
0.03 1
 
4.5%
0.07 1
 
4.5%
0.1 1
 
4.5%
0.13 1
 
4.5%
0.15 1
 
4.5%
0.19 1
 
4.5%
0.21 1
 
4.5%
ValueCountFrequency (%)
26.21 1
4.5%
14.7 1
4.5%
9.93 1
4.5%
5.57 1
4.5%
2.58 1
4.5%
0.93 1
4.5%
0.28 1
4.5%
0.24 1
4.5%
0.23 1
4.5%
0.21 1
4.5%

용도구역상세정보(ua_dt_dc)
Text

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T04:46:45.574108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length14.727273
Min length6

Characters and Unicode

Total characters324
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)27.3%

Sample

1st row임랑해수욕장
2nd row임랑해수욕장
3rd row해수욕장기능구 해수욕장구 일광해수욕장
4th row낚시어선밀집구역 레저관광구
5th row해수욕장기능구 낚시어선밀집구역 해수욕장구
ValueCountFrequency (%)
낚시어선밀집구역 14
32.6%
해수욕장기능구 4
 
9.3%
마리나 3
 
7.0%
레저관광구 3
 
7.0%
해수욕장구 3
 
7.0%
마리나예정시설 2
 
4.7%
해운대해수욕장 2
 
4.7%
수영만 2
 
4.7%
임랑해수욕장 2
 
4.7%
일광해수욕장 1
 
2.3%
Other values (7) 7
16.3%
2023-12-13T04:46:46.045271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
 
12.0%
구 26
 
8.0%
수 18
 
5.6%
해 17
 
5.2%
역 16
 
4.9%
시 16
 
4.9%
장 15
 
4.6%
욕 15
 
4.6%
낚 14
 
4.3%
어 14
 
4.3%
Other values (40) 134
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 280
86.4%
Space Separator 39
 
12.0%
Decimal Number 3
 
0.9%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 26
 
9.3%
수 18
 
6.4%
해 17
 
6.1%
역 16
 
5.7%
시 16
 
5.7%
장 15
 
5.4%
욕 15
 
5.4%
낚 14
 
5.0%
어 14
 
5.0%
선 14
 
5.0%
Other values (35) 115
41.1%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
0 1
33.3%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 280
86.4%
Common 44
 
13.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 26
 
9.3%
수 18
 
6.4%
해 17
 
6.1%
역 16
 
5.7%
시 16
 
5.7%
장 15
 
5.4%
욕 15
 
5.4%
낚 14
 
5.0%
어 14
 
5.0%
선 14
 
5.0%
Other values (35) 115
41.1%
Common
ValueCountFrequency (%)
39
88.6%
1 2
 
4.5%
) 1
 
2.3%
( 1
 
2.3%
0 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 280
86.4%
ASCII 44
 
13.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39
88.6%
1 2
 
4.5%
) 1
 
2.3%
( 1
 
2.3%
0 1
 
2.3%
Hangul
ValueCountFrequency (%)
구 26
 
9.3%
수 18
 
6.4%
해 17
 
6.1%
역 16
 
5.7%
시 16
 
5.7%
장 15
 
5.4%
욕 15
 
5.4%
낚 14
 
5.0%
어 14
 
5.0%
선 14
 
5.0%
Other values (35) 115
41.1%

Interactions

2023-12-13T04:46:43.606524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:46:43.412267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:46:43.716678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:46:43.500454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:46:46.177726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간정보일련번호(gid)용도구역아이디(ua_id)용도구역면적(ua_ar)용도구역상세정보(ua_dt_dc)
공간정보일련번호(gid)1.0001.0000.0000.883
용도구역아이디(ua_id)1.0001.0001.0001.000
용도구역면적(ua_ar)0.0001.0001.0000.000
용도구역상세정보(ua_dt_dc)0.8831.0000.0001.000
2023-12-13T04:46:46.317679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간정보일련번호(gid)용도구역면적(ua_ar)
공간정보일련번호(gid)1.0000.701
용도구역면적(ua_ar)0.7011.000

Missing values

2023-12-13T04:46:43.856242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:46:43.957967image/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

공간정보일련번호(gid)용도구역아이디(ua_id)용도구역면적(ua_ar)용도구역상세정보(ua_dt_dc)
01TO351916N1291603E20200.01임랑해수욕장
12TO351908N1291552E20200.02임랑해수욕장
23TO351539N1291407E20200.15해수욕장기능구 해수욕장구 일광해수욕장
38TO350949N1291146E20200.03낚시어선밀집구역 레저관광구
44TO351042N1291204E20200.21해수욕장기능구 낚시어선밀집구역 해수욕장구
55TO351045N1291220E20200.0해수욕장기능구 낚시어선밀집구역 해수욕장구
66TO351021N1291206E20200.07해수욕장기능구 낚시어선밀집구역
77TO351008N1291151E20200.0낚시어선밀집구역 레저관광구
89TO351001N1291150E20200.0낚시어선밀집구역 레저관광구
910TO350856N1290732E20202.58광안리해수욕장 남천마리나 수영만관광개발구역
공간정보일련번호(gid)용도구역아이디(ua_id)용도구역면적(ua_ar)용도구역상세정보(ua_dt_dc)
1213TO350916N1290857E20200.1더베이101(운촌항) 마리나 운촌마리나예정구역
1314TO350926N1290824E20200.0수영만 마리나
1415TO350920N1290945E20200.23마리나예정시설 낚시어선밀집구역 해운대해수욕장
1518TO345932N1284612E20205.57낚시어선밀집구역
1619TO345142N1290050E202026.21낚시어선밀집구역
1720TO345321N1290257E20200.93낚시어선밀집구역
1821TO345128N1284537E20209.93낚시어선밀집구역
1916TO350429N1290110E20200.24송도해수욕장
2017TO350248N1285755E20200.19낚시어선밀집구역 다대포해수욕장
2122TO344306N1285353E202014.7낚시어선밀집구역