Overview

Dataset statistics

Number of variables5
Number of observations208
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 KiB
Average record size in memory44.6 B

Variable types

Categorical1
Numeric3
Text1

Dataset

Description전국 지자체 농지전용 허가(협의·신고) 통계정보
Author한국농어촌공사
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220211000000001839

Alerts

년도 has constant value ""Constant
건수 is highly overall correlated with 면적High correlation
면적 is highly overall correlated with 건수High correlation
지역코드 has unique valuesUnique
시군구 has unique valuesUnique
면적 has unique valuesUnique

Reproduction

Analysis started2024-04-19 06:47:25.178511
Analysis finished2024-04-19 06:47:26.238287
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2016
208 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 208
100.0%

Length

2024-04-19T15:47:26.288527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:47:26.370479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 208
100.0%

지역코드
Real number (ℝ)

UNIQUE 

Distinct208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42027.014
Minimum11500
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-19T15:47:26.472402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11500
5-th percentile27240.5
Q141286.5
median43765
Q346872.5
95-th percentile48726.5
Maximum50130
Range38630
Interquartile range (IQR)5586

Descriptive statistics

Standard deviation7045.6471
Coefficient of variation (CV)0.16764567
Kurtosis2.8437503
Mean42027.014
Median Absolute Deviation (MAD)2633
Skewness-1.7069303
Sum8741619
Variance49641143
MonotonicityStrictly increasing
2024-04-19T15:47:26.602446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11500 1
 
0.5%
43800 1
 
0.5%
45740 1
 
0.5%
45750 1
 
0.5%
45770 1
 
0.5%
45790 1
 
0.5%
45800 1
 
0.5%
46110 1
 
0.5%
46130 1
 
0.5%
46150 1
 
0.5%
Other values (198) 198
95.2%
ValueCountFrequency (%)
11500 1
0.5%
11710 1
0.5%
26260 1
0.5%
26290 1
0.5%
26380 1
0.5%
26410 1
0.5%
26440 1
0.5%
26710 1
0.5%
27140 1
0.5%
27170 1
0.5%
ValueCountFrequency (%)
50130 1
0.5%
50110 1
0.5%
48890 1
0.5%
48880 1
0.5%
48870 1
0.5%
48860 1
0.5%
48850 1
0.5%
48840 1
0.5%
48820 1
0.5%
48740 1
0.5%

시군구
Text

UNIQUE 

Distinct208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-19T15:47:26.902606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length10
Mean length10.370192
Min length9

Characters and Unicode

Total characters2157
Distinct characters140
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

Unique208 ?
Unique (%)100.0%

Sample

1st row서울특별시 강서구
2nd row서울특별시 송파구
3rd row부산광역시 동래구
4th row부산광역시 남구
5th row부산광역시 사하구
ValueCountFrequency (%)
경기도 40
 
9.5%
경상북도 24
 
5.7%
전라남도 22
 
5.2%
경상남도 22
 
5.2%
강원도 18
 
4.3%
전라북도 16
 
3.8%
충청남도 16
 
3.8%
충청북도 14
 
3.3%
인천광역시 7
 
1.7%
부산광역시 6
 
1.4%
Other values (202) 236
56.1%
2024-04-19T15:47:27.336263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
628
29.1%
도 177
 
8.2%
시 132
 
6.1%
경 89
 
4.1%
군 84
 
3.9%
남 73
 
3.4%
구 70
 
3.2%
북 59
 
2.7%
상 49
 
2.3%
전 45
 
2.1%
Other values (130) 751
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1525
70.7%
Space Separator 628
29.1%
Decimal Number 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 177
 
11.6%
시 132
 
8.7%
경 89
 
5.8%
군 84
 
5.5%
남 73
 
4.8%
구 70
 
4.6%
북 59
 
3.9%
상 49
 
3.2%
전 45
 
3.0%
광 42
 
2.8%
Other values (126) 705
46.2%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
5 1
25.0%
2 1
25.0%
Space Separator
ValueCountFrequency (%)
628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1525
70.7%
Common 632
29.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 177
 
11.6%
시 132
 
8.7%
경 89
 
5.8%
군 84
 
5.5%
남 73
 
4.8%
구 70
 
4.6%
북 59
 
3.9%
상 49
 
3.2%
전 45
 
3.0%
광 42
 
2.8%
Other values (126) 705
46.2%
Common
ValueCountFrequency (%)
628
99.4%
0 2
 
0.3%
5 1
 
0.2%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1525
70.7%
ASCII 632
29.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
628
99.4%
0 2
 
0.3%
5 1
 
0.2%
2 1
 
0.2%
Hangul
ValueCountFrequency (%)
도 177
 
11.6%
시 132
 
8.7%
경 89
 
5.8%
군 84
 
5.5%
남 73
 
4.8%
구 70
 
4.6%
북 59
 
3.9%
상 49
 
3.2%
전 45
 
3.0%
광 42
 
2.8%
Other values (126) 705
46.2%

건수
Real number (ℝ)

HIGH CORRELATION 

Distinct189
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551.375
Minimum3
Maximum6854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-19T15:47:27.464129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q1149.5
median368.5
Q3703
95-th percentile1725.85
Maximum6854
Range6851
Interquartile range (IQR)553.5

Descriptive statistics

Standard deviation691.05056
Coefficient of variation (CV)1.2533223
Kurtosis34.455213
Mean551.375
Median Absolute Deviation (MAD)249.5
Skewness4.5776193
Sum114686
Variance477550.88
MonotonicityNot monotonic
2024-04-19T15:47:27.594170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 2
 
1.0%
585 2
 
1.0%
205 2
 
1.0%
14 2
 
1.0%
313 2
 
1.0%
16 2
 
1.0%
3 2
 
1.0%
162 2
 
1.0%
55 2
 
1.0%
11 2
 
1.0%
Other values (179) 188
90.4%
ValueCountFrequency (%)
3 2
1.0%
5 1
0.5%
6 2
1.0%
7 2
1.0%
8 1
0.5%
10 2
1.0%
11 2
1.0%
12 2
1.0%
14 2
1.0%
15 1
0.5%
ValueCountFrequency (%)
6854 1
0.5%
3550 1
0.5%
2421 1
0.5%
2324 1
0.5%
2232 1
0.5%
2022 1
0.5%
1945 1
0.5%
1942 1
0.5%
1926 1
0.5%
1770 1
0.5%

면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329591.21
Minimum262
Maximum4154372.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-19T15:47:27.733235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum262
5-th percentile4815.065
Q185279.775
median222188.62
Q3406325.59
95-th percentile921707.82
Maximum4154372.7
Range4154110.7
Interquartile range (IQR)321045.82

Descriptive statistics

Standard deviation451186.11
Coefficient of variation (CV)1.3689264
Kurtosis34.413474
Mean329591.21
Median Absolute Deviation (MAD)150679.45
Skewness4.9080694
Sum68554972
Variance2.035689 × 1011
MonotonicityNot monotonic
2024-04-19T15:47:27.853552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4446.62 1
 
0.5%
152824.0 1
 
0.5%
168490.7 1
 
0.5%
193582.5 1
 
0.5%
146459.05 1
 
0.5%
350659.4 1
 
0.5%
136909.2 1
 
0.5%
89323.0 1
 
0.5%
261658.5 1
 
0.5%
455156.41 1
 
0.5%
Other values (198) 198
95.2%
ValueCountFrequency (%)
262.0 1
0.5%
1874.0 1
0.5%
1947.7 1
0.5%
2070.0 1
0.5%
2592.0 1
0.5%
2802.0 1
0.5%
2818.0 1
0.5%
3354.0 1
0.5%
3701.0 1
0.5%
4446.62 1
0.5%
ValueCountFrequency (%)
4154372.66 1
0.5%
3374377.96 1
0.5%
2029384.59 1
0.5%
1213842.3 1
0.5%
1139032.0 1
0.5%
1136084.7 1
0.5%
1076062.0 1
0.5%
1004260.2 1
0.5%
986624.9 1
0.5%
964635.7 1
0.5%

Interactions

2024-04-19T15:47:25.848887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:25.312422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:25.575047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:25.947977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:25.406969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:25.664876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:26.034142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:25.499312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:25.747989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T15:47:27.930616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드건수면적
지역코드1.0000.0300.150
건수0.0301.0000.983
면적0.1500.9831.000
2024-04-19T15:47:28.009670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드건수면적
지역코드1.0000.3740.351
건수0.3741.0000.972
면적0.3510.9721.000

Missing values

2024-04-19T15:47:26.127301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T15:47:26.206951image/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

년도지역코드시군구건수면적
0201611500서울특별시 강서구84446.62
1201611710서울특별시 송파구78366.0
2201626260부산광역시 동래구186669.0
3201626290부산광역시 남구122802.0
4201626380부산광역시 사하구1010960.0
5201626410부산광역시 금정구61874.0
6201626440부산광역시 강서구155114.0
7201626710부산광역시 기장군448300096.04
8201627140대구광역시 동구19198132.39
9201627170대구광역시 서구123701.0
년도지역코드시군구건수면적
198201648740경상남도 창녕군701354146.0
199201648820경상남도 고성군694356851.4
200201648840경상남도 남해군500211486.0
201201648850경상남도 하동군601229340.3
202201648860경상남도 산청군482230028.55
203201648870경상남도 함양군446221427.44
204201648880경상남도 거창군423258775.5
205201648890경상남도 합천군503266718.9
206201650110제주특별자치도 제주시35503374377.96
207201650130제주특별자치도 서귀포시24212029384.59