Overview

Dataset statistics

Number of variables5
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory45.4 B

Variable types

Categorical1
Text2
Numeric2

Dataset

Description제주특별자치도개발공사에서 제공하는 제주도 토지이용 현황(나지, 농업지역, 수역, 습지, 건조지역, 초지 등) 입니다.
Author제주특별자치도개발공사
URLhttps://www.data.go.kr/data/15089150/fileData.do

Alerts

면적 is highly overall correlated with 둘레High correlation
둘레 is highly overall correlated with 면적High correlation
소분류 has unique valuesUnique
면적 has unique valuesUnique
둘레 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:20:29.210521
Analysis finished2023-12-12 04:20:30.132122
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

Distinct7
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
시가화 건조지역
14 
농업지역
7 
나지
6 
초지
4 
산림지역
3 
Other values (2)
5 

Length

Max length8
Median length4
Mean length4.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시가화 건조지역 14
35.9%
농업지역 7
17.9%
나지 6
15.4%
초지 4
 
10.3%
산림지역 3
 
7.7%
수역 3
 
7.7%
습지 2
 
5.1%

Length

2023-12-12T13:20:30.248445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:20:30.410125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시가화 14
26.4%
건조지역 14
26.4%
농업지역 7
13.2%
나지 6
11.3%
초지 4
 
7.5%
산림지역 3
 
5.7%
수역 3
 
5.7%
습지 2
 
3.8%

중분류
Text

Distinct22
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T13:20:30.635129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.974359
Min length1

Characters and Unicode

Total characters155
Distinct characters42
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

Unique12 ?
Unique (%)30.8%

Sample

1st row인공나지
2nd row인공나지
3rd row인공나지
4th row자연나지
5th row자연나지
ValueCountFrequency (%)
교통지역 5
12.8%
자연나지 3
 
7.7%
공공시설지역 3
 
7.7%
인공나지 3
 
7.7%
인공초지 3
 
7.7%
기타재배지 2
 
5.1%
주거지역 2
 
5.1%
밭 2
 
5.1%
상업지역 2
 
5.1%
내륙수 2
 
5.1%
Other values (12) 12
30.8%
2023-12-12T13:20:30.987675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 28
18.1%
역 13
 
8.4%
공 13
 
8.4%
인 6
 
3.9%
나 6
 
3.9%
수 6
 
3.9%
교 5
 
3.2%
연 5
 
3.2%
시 5
 
3.2%
설 5
 
3.2%
Other values (32) 63
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 155
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 28
18.1%
역 13
 
8.4%
공 13
 
8.4%
인 6
 
3.9%
나 6
 
3.9%
수 6
 
3.9%
교 5
 
3.2%
연 5
 
3.2%
시 5
 
3.2%
설 5
 
3.2%
Other values (32) 63
40.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 155
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 28
18.1%
역 13
 
8.4%
공 13
 
8.4%
인 6
 
3.9%
나 6
 
3.9%
수 6
 
3.9%
교 5
 
3.2%
연 5
 
3.2%
시 5
 
3.2%
설 5
 
3.2%
Other values (32) 63
40.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 155
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 28
18.1%
역 13
 
8.4%
공 13
 
8.4%
인 6
 
3.9%
나 6
 
3.9%
수 6
 
3.9%
교 5
 
3.2%
연 5
 
3.2%
시 5
 
3.2%
설 5
 
3.2%
Other values (32) 63
40.6%

소분류
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T13:20:31.288026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.6923077
Min length2

Characters and Unicode

Total characters183
Distinct characters82
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

Unique39 ?
Unique (%)100.0%

Sample

1st row기타나지
2nd row운동장
3rd row채광지역
4th row강기슭
5th row암벽 바위
ValueCountFrequency (%)
된 3
 
5.4%
경지정리가 3
 
5.4%
기타 2
 
3.6%
밭 2
 
3.6%
안 2
 
3.6%
갯벌 1
 
1.8%
공공시설 1
 
1.8%
환경기초시설 1
 
1.8%
공업시설 1
 
1.8%
공항 1
 
1.8%
Other values (39) 39
69.6%
2023-12-12T13:20:31.775918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
9.3%
지 12
 
6.6%
시 10
 
5.5%
설 10
 
5.5%
기 7
 
3.8%
타 5
 
2.7%
공 5
 
2.7%
장 4
 
2.2%
수 4
 
2.2%
경 4
 
2.2%
Other values (72) 105
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
90.7%
Space Separator 17
 
9.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 12
 
7.2%
시 10
 
6.0%
설 10
 
6.0%
기 7
 
4.2%
타 5
 
3.0%
공 5
 
3.0%
장 4
 
2.4%
수 4
 
2.4%
경 4
 
2.4%
정 4
 
2.4%
Other values (71) 101
60.8%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
90.7%
Common 17
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 12
 
7.2%
시 10
 
6.0%
설 10
 
6.0%
기 7
 
4.2%
타 5
 
3.0%
공 5
 
3.0%
장 4
 
2.4%
수 4
 
2.4%
경 4
 
2.4%
정 4
 
2.4%
Other values (71) 101
60.8%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
90.7%
ASCII 17
 
9.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
100.0%
Hangul
ValueCountFrequency (%)
지 12
 
7.2%
시 10
 
6.0%
설 10
 
6.0%
기 7
 
4.2%
타 5
 
3.0%
공 5
 
3.0%
장 4
 
2.4%
수 4
 
2.4%
경 4
 
2.4%
정 4
 
2.4%
Other values (71) 101
60.8%

면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61503150
Minimum1307.5329
Maximum5.3442336 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:20:31.995718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1307.5329
5-th percentile30234.384
Q1882447.51
median5038875.3
Q335172767
95-th percentile3.4355463 × 108
Maximum5.3442336 × 108
Range5.3442206 × 108
Interquartile range (IQR)34290320

Descriptive statistics

Standard deviation1.2565983 × 108
Coefficient of variation (CV)2.0431447
Kurtosis5.6810482
Mean61503150
Median Absolute Deviation (MAD)4980686
Skewness2.4704288
Sum2.3986229 × 109
Variance1.5790394 × 1016
MonotonicityNot monotonic
2023-12-12T13:20:32.185061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
40920497.22 1
 
2.6%
200887.845 1
 
2.6%
5683777.568 1
 
2.6%
171210.1656 1
 
2.6%
979645.5735 1
 
2.6%
1307.532852 1
 
2.6%
58189.34229 1
 
2.6%
99574343.24 1
 
2.6%
38781.15402 1
 
2.6%
1686502.77 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1307.532852 1
2.6%
27249.09342 1
2.6%
30566.08322 1
2.6%
38781.15402 1
2.6%
58189.34229 1
2.6%
171210.1656 1
2.6%
200887.845 1
2.6%
223184.9661 1
2.6%
341049.734 1
2.6%
785249.4391 1
2.6%
ValueCountFrequency (%)
534423364.4 1
2.6%
395603076.4 1
2.6%
337771465.8 1
2.6%
292544398.3 1
2.6%
213996774.3 1
2.6%
188148433.9 1
2.6%
99574343.24 1
2.6%
83660672.08 1
2.6%
61983382.91 1
2.6%
40920497.22 1
2.6%

둘레
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3600010
Minimum150.89527
Maximum28902517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:20:32.348825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150.89527
5-th percentile3342.6798
Q193406.135
median527233.2
Q33920878.8
95-th percentile20796913
Maximum28902517
Range28902366
Interquartile range (IQR)3827472.7

Descriptive statistics

Standard deviation6857420.9
Coefficient of variation (CV)1.9048338
Kurtosis6.5530355
Mean3600010
Median Absolute Deviation (MAD)517119.68
Skewness2.6134329
Sum1.4040039 × 108
Variance4.7024221 × 1013
MonotonicityNot monotonic
2023-12-12T13:20:32.508749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
8956110.66 1
 
2.6%
14002.50799 1
 
2.6%
1023956.852 1
 
2.6%
27909.85488 1
 
2.6%
144411.7201 1
 
2.6%
150.895275 1
 
2.6%
7872.427998 1
 
2.6%
24936155.28 1
 
2.6%
16031.72669 1
 
2.6%
177336.2528 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
150.895275 1
2.6%
2514.210532 1
2.6%
3434.731948 1
2.6%
7872.427998 1
2.6%
10113.51908 1
2.6%
14002.50799 1
2.6%
16031.72669 1
2.6%
27909.85488 1
2.6%
46741.46759 1
2.6%
56580.58771 1
2.6%
ValueCountFrequency (%)
28902517.17 1
2.6%
24936155.28 1
2.6%
20336996.81 1
2.6%
11037195.29 1
2.6%
8956110.66 1
2.6%
8653959.439 1
2.6%
7043044.157 1
2.6%
4915542.343 1
2.6%
4602029.094 1
2.6%
4383155.237 1
2.6%

Interactions

2023-12-12T13:20:29.704792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.462148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.809626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.580937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:20:32.654211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류면적둘레
대분류1.0001.0001.0000.5210.614
중분류1.0001.0001.0000.9290.000
소분류1.0001.0001.0001.0001.000
면적0.5210.9291.0001.0000.908
둘레0.6140.0001.0000.9081.000
2023-12-12T13:20:32.780944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적둘레대분류
면적1.0000.9470.307
둘레0.9471.0000.252
대분류0.3070.2521.000

Missing values

2023-12-12T13:20:29.940899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:20:30.078239image/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

대분류중분류소분류면적둘레
0나지인공나지기타나지40920497.228956110.66
1나지인공나지운동장200887.84514002.50799
2나지인공나지채광지역1255647.50446741.46759
3나지자연나지강기슭341049.73456580.58771
4나지자연나지암벽 바위17305838.121446365.061
5나지자연나지해변2257018.764130231.6817
6농업지역과수원과수원188148433.911037195.29
7농업지역기타재배지기타재배지3429152.669234098.7352
8농업지역기타재배지목장 양식장5038875.3527233.2035
9농업지역논경지정리가 안 된 논223184.966110113.51908
대분류중분류소분류면적둘레
29시가화 건조지역교통지역항만1686502.77177336.2528
30시가화 건조지역문화체육휴양시설문화 체육 휴양시설5426611.302916249.4259
31시가화 건조지역상업지역상업 업무시설14795743.073458602.419
32시가화 건조지역상업지역혼합지역27249.093423434.731948
33시가화 건조지역주거지역공동주거시설4029676.02906262.5048
34시가화 건조지역주거지역단독주거시설16851254.594602029.094
35초지인공초지골프장12932364.62853643.3224
36초지인공초지기타초지292544398.328902517.17
37초지인공초지묘지29425037.194915542.343
38초지자연초지자연초지9976165.223152957.9216