Overview

Dataset statistics

Number of variables4
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory39.6 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description해양수산부통계조사에 수록된 제주특별자치도 해양수산업 사업체 현황 데이터로, 2019년 제주특별자치도 내 해양수산업 사업체 구분에 따른 비율 정보를 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15096979/fileData.do

Alerts

연도 has constant value ""Constant
사업체 구분 has unique valuesUnique
제주권(비율) has 1 (3.4%) zerosZeros

Reproduction

Analysis started2023-12-12 23:05:12.659283
Analysis finished2023-12-12 23:05:13.301695
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
2019
29 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 29
100.0%

Length

2023-12-13T08:05:13.359255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:05:13.447747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 29
100.0%

사업체 구분
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T08:05:13.623205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length11.37931
Min length3

Characters and Unicode

Total characters330
Distinct characters87
Distinct categories3 ?
Distinct scripts2 ?
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 row해양자원 생산, 공급 및 개발업
2nd row해양바이오 제품 제조업
3rd row항만 및 해상 교량 건설업
4th row해양수산플랜트 및 구조물 공사업
5th row해운업
ValueCountFrequency (%)
및 13
 
14.1%
해양수산 5
 
5.4%
제조업 4
 
4.3%
기자재 4
 
4.3%
수리업 3
 
3.3%
수산물 3
 
3.3%
서비스업 3
 
3.3%
구조물 2
 
2.2%
건조 2
 
2.2%
도소매업 2
 
2.2%
Other values (49) 51
55.4%
2023-12-13T08:05:13.942226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63
19.1%
업 28
 
8.5%
해 17
 
5.2%
수 17
 
5.2%
양 16
 
4.8%
산 15
 
4.5%
및 13
 
3.9%
조 8
 
2.4%
물 7
 
2.1%
기 6
 
1.8%
Other values (77) 140
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
80.0%
Space Separator 63
 
19.1%
Other Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 28
 
10.6%
해 17
 
6.4%
수 17
 
6.4%
양 16
 
6.1%
산 15
 
5.7%
및 13
 
4.9%
조 8
 
3.0%
물 7
 
2.7%
기 6
 
2.3%
제 5
 
1.9%
Other values (75) 132
50.0%
Space Separator
ValueCountFrequency (%)
63
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
80.0%
Common 66
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 28
 
10.6%
해 17
 
6.4%
수 17
 
6.4%
양 16
 
6.1%
산 15
 
5.7%
및 13
 
4.9%
조 8
 
3.0%
물 7
 
2.7%
기 6
 
2.3%
제 5
 
1.9%
Other values (75) 132
50.0%
Common
ValueCountFrequency (%)
63
95.5%
, 3
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
80.0%
ASCII 66
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63
95.5%
, 3
 
4.5%
Hangul
ValueCountFrequency (%)
업 28
 
10.6%
해 17
 
6.4%
수 17
 
6.4%
양 16
 
6.1%
산 15
 
5.7%
및 13
 
4.9%
조 8
 
3.0%
물 7
 
2.7%
기 6
 
2.3%
제 5
 
1.9%
Other values (75) 132
50.0%

사례수(개)
Real number (ℝ)

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5784.4483
Minimum47
Maximum49057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T08:05:14.066933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile70.2
Q1184
median1234
Q34293
95-th percentile35543.8
Maximum49057
Range49010
Interquartile range (IQR)4109

Descriptive statistics

Standard deviation12257.2
Coefficient of variation (CV)2.1189921
Kurtosis6.9840341
Mean5784.4483
Median Absolute Deviation (MAD)1096
Skewness2.7581244
Sum167749
Variance1.5023896 × 108
MonotonicityNot monotonic
2023-12-13T08:05:14.187310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
81 2
 
6.9%
63 1
 
3.4%
205 1
 
3.4%
49057 1
 
3.4%
660 1
 
3.4%
1617 1
 
3.4%
1283 1
 
3.4%
191 1
 
3.4%
138 1
 
3.4%
1234 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
47 1
3.4%
63 1
3.4%
81 2
6.9%
83 1
3.4%
138 1
3.4%
169 1
3.4%
184 1
3.4%
191 1
3.4%
205 1
3.4%
442 1
3.4%
ValueCountFrequency (%)
49057 1
3.4%
40893 1
3.4%
27520 1
3.4%
14666 1
3.4%
5843 1
3.4%
5315 1
3.4%
4867 1
3.4%
4293 1
3.4%
2614 1
3.4%
1617 1
3.4%

제주권(비율)
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1034483
Minimum0
Maximum56.6
Zeros1
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T08:05:14.288535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.34
Q11.8
median2.6
Q33.7
95-th percentile11.94
Maximum56.6
Range56.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation10.295682
Coefficient of variation (CV)2.0173971
Kurtosis24.381591
Mean5.1034483
Median Absolute Deviation (MAD)1.1
Skewness4.7938113
Sum148
Variance106.00106
MonotonicityNot monotonic
2023-12-13T08:05:14.394942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.3 2
 
6.9%
3.1 2
 
6.9%
3.7 2
 
6.9%
2.4 2
 
6.9%
11.1 1
 
3.4%
2.7 1
 
3.4%
4.7 1
 
3.4%
1.8 1
 
3.4%
0.7 1
 
3.4%
2.5 1
 
3.4%
Other values (15) 15
51.7%
ValueCountFrequency (%)
0.0 1
3.4%
0.1 1
3.4%
0.7 1
3.4%
1.0 1
3.4%
1.1 1
3.4%
1.2 1
3.4%
1.6 1
3.4%
1.8 1
3.4%
2.1 1
3.4%
2.3 2
6.9%
ValueCountFrequency (%)
56.6 1
3.4%
12.5 1
3.4%
11.1 1
3.4%
7.4 1
3.4%
4.7 1
3.4%
4.5 1
3.4%
4.3 1
3.4%
3.7 2
6.9%
3.6 1
3.4%
3.1 2
6.9%

Interactions

2023-12-13T08:05:12.982820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:05:12.794540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:05:13.080251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:05:12.887942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:05:14.473967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업체 구분사례수(개)제주권(비율)
사업체 구분1.0001.0001.000
사례수(개)1.0001.0000.000
제주권(비율)1.0000.0001.000
2023-12-13T08:05:14.570378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수(개)제주권(비율)
사례수(개)1.000-0.322
제주권(비율)-0.3221.000

Missing values

2023-12-13T08:05:13.192460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:05:13.271559image/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

연도사업체 구분사례수(개)제주권(비율)
02019해양자원 생산, 공급 및 개발업6311.1
12019해양바이오 제품 제조업1844.3
22019항만 및 해상 교량 건설업817.4
32019해양수산플랜트 및 구조물 공사업1693.6
42019해운업48671.1
52019항만업26141.0
62019선박 건조 및 수리업14432.3
72019해양 플랜트, 구조물 건조 및 수리업472.1
82019선박 및 해양플랜트 부분품 제조업42930.1
92019어로어업275204.5
연도사업체 구분사례수(개)제주권(비율)
192019수산 기자재 제조업11303.7
202019해양폐기물 처리 및 정화복원업813.7
212019해양수산 기자재 도소매업53153.1
222019해양수산 기자재 수리업12342.5
232019해양수산인력 고용 알선 및 공급업1380.7
242019해양수산교육 서비스업1913.1
252019해양수산 전문, 과학 및 기술 서비스업12831.8
262019해양수산 금융 및 보험업16172.4
272019해양수산 협회 및 단체6604.7
282019수산물 요리 전문점490572.3