Overview

Dataset statistics

Number of variables5
Number of observations235
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.0 KiB
Average record size in memory43.6 B

Variable types

Numeric3
Categorical1
Text1

Dataset

Description2022년 6월 기준 전국 시군구별 임차농지현황에 대한 csv 데이터로 시군구 코드, 시군구명, 필지수, 면적에 대한 내용을 제공합니다.
Author한국농어촌공사
URLhttps://www.data.go.kr/data/15100759/fileData.do

Alerts

시군구코드 is highly overall correlated with 총_필지수 and 2 other fieldsHigh correlation
총_필지수 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
총_면적 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
시도 is highly overall correlated with 시군구코드High correlation
시군구코드 has unique valuesUnique
총_면적 has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:51:03.193020
Analysis finished2023-12-11 22:51:04.163282
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct235
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39593.26
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T07:51:04.234180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11701
Q133910
median42810
Q346795
95-th percentile48447
Maximum50130
Range39020
Interquartile range (IQR)12885

Descriptive statistics

Standard deviation9928.4103
Coefficient of variation (CV)0.25076011
Kurtosis1.4572865
Mean39593.26
Median Absolute Deviation (MAD)4000
Skewness-1.4903025
Sum9304416
Variance98573332
MonotonicityStrictly increasing
2023-12-12T07:51:04.392108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11110 1
 
0.4%
45770 1
 
0.4%
45111 1
 
0.4%
45113 1
 
0.4%
45130 1
 
0.4%
45140 1
 
0.4%
45180 1
 
0.4%
45190 1
 
0.4%
45210 1
 
0.4%
45710 1
 
0.4%
Other values (225) 225
95.7%
ValueCountFrequency (%)
11110 1
0.4%
11260 1
0.4%
11305 1
0.4%
11320 1
0.4%
11350 1
0.4%
11380 1
0.4%
11470 1
0.4%
11500 1
0.4%
11530 1
0.4%
11620 1
0.4%
ValueCountFrequency (%)
50130 1
0.4%
50110 1
0.4%
48890 1
0.4%
48880 1
0.4%
48870 1
0.4%
48860 1
0.4%
48850 1
0.4%
48840 1
0.4%
48820 1
0.4%
48740 1
0.4%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
경기도
42 
경상북도
24 
경상남도
22 
전라남도
22 
강원도
18 
Other values (12)
107 

Length

Max length7
Median length5
Mean length4.0340426
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 42
17.9%
경상북도 24
10.2%
경상남도 22
9.4%
전라남도 22
9.4%
강원도 18
7.7%
충청남도 16
 
6.8%
전라북도 15
 
6.4%
부산광역시 14
 
6.0%
서울특별시 14
 
6.0%
충청북도 14
 
6.0%
Other values (7) 34
14.5%

Length

2023-12-12T07:51:04.542655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 42
17.9%
경상북도 24
10.2%
경상남도 22
9.4%
전라남도 22
9.4%
강원도 18
7.7%
충청남도 16
 
6.8%
전라북도 15
 
6.4%
충청북도 14
 
6.0%
서울특별시 14
 
6.0%
부산광역시 14
 
6.0%
Other values (7) 34
14.5%

시군구
Text

Distinct217
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T07:51:04.842203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3829787
Min length2

Characters and Unicode

Total characters795
Distinct characters141
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

Unique210 ?
Unique (%)89.4%

Sample

1st row종로구
2nd row중랑구
3rd row강북구
4th row도봉구
5th row노원구
ValueCountFrequency (%)
동구 5
 
2.1%
서구 5
 
2.1%
청주시 4
 
1.7%
북구 4
 
1.7%
남구 4
 
1.7%
중구 3
 
1.3%
강서구 2
 
0.8%
고성군 2
 
0.8%
무주군 1
 
0.4%
장수군 1
 
0.4%
Other values (208) 208
87.0%
2023-12-12T07:51:05.260742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 100
 
12.6%
구 91
 
11.4%
군 85
 
10.7%
주 24
 
3.0%
양 22
 
2.8%
천 22
 
2.8%
산 22
 
2.8%
성 19
 
2.4%
원 18
 
2.3%
안 17
 
2.1%
Other values (131) 375
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 791
99.5%
Space Separator 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 100
 
12.6%
구 91
 
11.5%
군 85
 
10.7%
주 24
 
3.0%
양 22
 
2.8%
천 22
 
2.8%
산 22
 
2.8%
성 19
 
2.4%
원 18
 
2.3%
안 17
 
2.1%
Other values (130) 371
46.9%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 791
99.5%
Common 4
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 100
 
12.6%
구 91
 
11.5%
군 85
 
10.7%
주 24
 
3.0%
양 22
 
2.8%
천 22
 
2.8%
산 22
 
2.8%
성 19
 
2.4%
원 18
 
2.3%
안 17
 
2.1%
Other values (130) 371
46.9%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 791
99.5%
ASCII 4
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 100
 
12.6%
구 91
 
11.5%
군 85
 
10.7%
주 24
 
3.0%
양 22
 
2.8%
천 22
 
2.8%
산 22
 
2.8%
성 19
 
2.4%
원 18
 
2.3%
안 17
 
2.1%
Other values (130) 371
46.9%
ASCII
ValueCountFrequency (%)
4
100.0%

총_필지수
Real number (ℝ)

HIGH CORRELATION 

Distinct233
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25032.757
Minimum1
Maximum87885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T07:51:05.414629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.4
Q12127
median20225
Q341437.5
95-th percentile71966.1
Maximum87885
Range87884
Interquartile range (IQR)39310.5

Descriptive statistics

Standard deviation23692.34
Coefficient of variation (CV)0.94645345
Kurtosis-0.34728457
Mean25032.757
Median Absolute Deviation (MAD)18427
Skewness0.73889175
Sum5882698
Variance5.6132696 × 108
MonotonicityNot monotonic
2023-12-12T07:51:05.531340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
0.9%
970 2
 
0.9%
21 1
 
0.4%
40727 1
 
0.4%
11321 1
 
0.4%
33877 1
 
0.4%
71790 1
 
0.4%
86544 1
 
0.4%
62969 1
 
0.4%
70615 1
 
0.4%
Other values (223) 223
94.9%
ValueCountFrequency (%)
1 2
0.9%
2 1
0.4%
3 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
11 1
0.4%
13 1
0.4%
14 1
0.4%
16 1
0.4%
ValueCountFrequency (%)
87885 1
0.4%
86544 1
0.4%
86380 1
0.4%
85735 1
0.4%
81733 1
0.4%
81239 1
0.4%
80660 1
0.4%
77346 1
0.4%
74641 1
0.4%
73958 1
0.4%

총_면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct235
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1615748 × 109
Minimum4988
Maximum4.1352567 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T07:51:05.660087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4988
5-th percentile224867.16
Q175133356
median3.0668366 × 109
Q39.0766108 × 109
95-th percentile2.4872346 × 1010
Maximum4.1352567 × 1010
Range4.1352562 × 1010
Interquartile range (IQR)9.0014775 × 109

Descriptive statistics

Standard deviation8.1154806 × 109
Coefficient of variation (CV)1.3171114
Kurtosis3.226917
Mean6.1615748 × 109
Median Absolute Deviation (MAD)3.0632517 × 109
Skewness1.7847131
Sum1.4479701 × 1012
Variance6.5861025 × 1019
MonotonicityNot monotonic
2023-12-12T07:51:05.845397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2238984.0 1
 
0.4%
9517473844.0 1
 
0.4%
511919118.1 1
 
0.4%
775121368.1 1
 
0.4%
10935315406.0 1
 
0.4%
23746160713.0 1
 
0.4%
32561351012.0 1
 
0.4%
20171288313.0 1
 
0.4%
25096025591.0 1
 
0.4%
8272649092.0 1
 
0.4%
Other values (225) 225
95.7%
ValueCountFrequency (%)
4988.0 1
0.4%
5607.0 1
0.4%
12740.0 1
0.4%
15548.0 1
0.4%
28062.25 1
0.4%
77612.29 1
0.4%
78883.96 1
0.4%
84318.0 1
0.4%
97004.4 1
0.4%
141424.0 1
0.4%
ValueCountFrequency (%)
41352567308.0 1
0.4%
39353351360.0 1
0.4%
32895332345.0 1
0.4%
32561351012.0 1
0.4%
31025600720.0 1
0.4%
30258642180.0 1
0.4%
26123768697.0 1
0.4%
26045614429.0 1
0.4%
25640556822.0 1
0.4%
25383467115.0 1
0.4%

Interactions

2023-12-12T07:51:03.814843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.368142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.605428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.894122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.454153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.683097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.958578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.531689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:51:03.745335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:51:05.945234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드시도총_필지수총_면적
시군구코드1.0000.9950.4580.324
시도0.9951.0000.5700.432
총_필지수0.4580.5701.0000.813
총_면적0.3240.4320.8131.000
2023-12-12T07:51:06.041263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드총_필지수총_면적시도
시군구코드1.0000.7110.6890.960
총_필지수0.7111.0000.9840.258
총_면적0.6890.9841.0000.185
시도0.9600.2580.1851.000

Missing values

2023-12-12T07:51:04.052749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:51:04.131130image/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

시군구코드시도시군구총_필지수총_면적
011110서울특별시종로구212238984.0
111260서울특별시중랑구56546712.18
211305서울특별시강북구928062.25
311320서울특별시도봉구93267735.15
411350서울특별시노원구32866641.74
511380서울특별시은평구1081203585.0
611470서울특별시양천구1178883.96
711500서울특별시강서구86827740027.42
811530서울특별시구로구88977482.44
911620서울특별시관악구14988.0
시군구코드시도시군구총_필지수총_면적
22548740경상남도창녕군4703610590178727.0
22648820경상남도고성군385136218329008.0
22748840경상남도남해군320672526748946.0
22848850경상남도하동군459626362968561.0
22948860경상남도산청군336425785470535.0
23048870경상남도함양군329885206195143.0
23148880경상남도거창군531768339909696.0
23248890경상남도합천군4444412050386108.0
23350110제주특별자치도제주시7734626123768697.0
23450130제주특별자치도서귀포시7174116402504921.0