Overview

Dataset statistics

Number of variables5
Number of observations227
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory43.6 B

Variable types

Numeric3
Categorical1
Text1

Dataset

Description2023년 3월말 기준 전국 시군구별 임차농지현황에 대한 csv데이터로 시군구 코드, 시군구명, 필지수, 면적에 대한 내용을 제공합니다.
URLhttps://www.data.go.kr/data/15069942/fileData.do

Alerts

시도코드 is highly overall correlated with 필지수 and 2 other fieldsHigh correlation
필지수 is highly overall correlated with 시도코드 and 1 other fieldsHigh correlation
면적(ha) is highly overall correlated with 시도코드 and 1 other fieldsHigh correlation
시도 is highly overall correlated with 시도코드High correlation
면적(ha) has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:53:07.402370
Analysis finished2023-12-12 14:53:08.907665
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.665198
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T23:53:08.961111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile15.5
Q141
median43
Q346
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.4916253
Coefficient of variation (CV)0.23929353
Kurtosis2.0984974
Mean39.665198
Median Absolute Deviation (MAD)3
Skewness-1.6447393
Sum9004
Variance90.090952
MonotonicityIncreasing
2023-12-12T23:53:09.113736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
41 41
18.1%
47 24
10.6%
48 22
9.7%
46 22
9.7%
42 18
7.9%
44 16
 
7.0%
45 15
 
6.6%
43 14
 
6.2%
11 12
 
5.3%
26 11
 
4.8%
Other values (7) 32
14.1%
ValueCountFrequency (%)
11 12
 
5.3%
26 11
 
4.8%
27 6
 
2.6%
28 8
 
3.5%
29 5
 
2.2%
30 5
 
2.2%
31 5
 
2.2%
36 1
 
0.4%
41 41
18.1%
42 18
7.9%
ValueCountFrequency (%)
50 2
 
0.9%
48 22
9.7%
47 24
10.6%
46 22
9.7%
45 15
 
6.6%
44 16
 
7.0%
43 14
 
6.2%
42 18
7.9%
41 41
18.1%
36 1
 
0.4%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
41 
경상북도
24 
경상남도
22 
전라남도
22 
강원도
18 
Other values (12)
100 

Length

Max length7
Median length5
Mean length4.0088106
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 41
18.1%
경상북도 24
10.6%
경상남도 22
9.7%
전라남도 22
9.7%
강원도 18
7.9%
충청남도 16
 
7.0%
전라북도 15
 
6.6%
충청북도 14
 
6.2%
서울특별시 12
 
5.3%
부산광역시 11
 
4.8%
Other values (7) 32
14.1%

Length

2023-12-12T23:53:09.291442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 41
18.1%
경상북도 24
10.6%
경상남도 22
9.7%
전라남도 22
9.7%
강원도 18
7.9%
충청남도 16
 
7.0%
전라북도 15
 
6.6%
충청북도 14
 
6.2%
서울특별시 12
 
5.3%
부산광역시 11
 
4.8%
Other values (7) 32
14.1%

시군구
Text

Distinct211
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T23:53:09.629175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3832599
Min length2

Characters and Unicode

Total characters768
Distinct characters136
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

Unique204 ?
Unique (%)89.9%

Sample

1st row강남구
2nd row강동구
3rd row강서구
4th row구로구
5th row노원구
ValueCountFrequency (%)
서구 5
 
2.2%
북구 4
 
1.7%
청주시 4
 
1.7%
동구 4
 
1.7%
남구 3
 
1.3%
중구 3
 
1.3%
고성군 2
 
0.9%
강서구 2
 
0.9%
고흥군 1
 
0.4%
청도군 1
 
0.4%
Other values (202) 202
87.4%
2023-12-12T23:53:10.153821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 99
 
12.9%
군 85
 
11.1%
구 83
 
10.8%
주 24
 
3.1%
천 22
 
2.9%
양 22
 
2.9%
산 21
 
2.7%
성 19
 
2.5%
원 17
 
2.2%
안 17
 
2.2%
Other values (126) 359
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 764
99.5%
Space Separator 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 99
 
13.0%
군 85
 
11.1%
구 83
 
10.9%
주 24
 
3.1%
천 22
 
2.9%
양 22
 
2.9%
산 21
 
2.7%
성 19
 
2.5%
원 17
 
2.2%
안 17
 
2.2%
Other values (125) 355
46.5%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 764
99.5%
Common 4
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 99
 
13.0%
군 85
 
11.1%
구 83
 
10.9%
주 24
 
3.1%
천 22
 
2.9%
양 22
 
2.9%
산 21
 
2.7%
성 19
 
2.5%
원 17
 
2.2%
안 17
 
2.2%
Other values (125) 355
46.5%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 764
99.5%
ASCII 4
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 99
 
13.0%
군 85
 
11.1%
구 83
 
10.9%
주 24
 
3.1%
천 22
 
2.9%
양 22
 
2.9%
산 21
 
2.7%
성 19
 
2.5%
원 17
 
2.2%
안 17
 
2.2%
Other values (125) 355
46.5%
ASCII
ValueCountFrequency (%)
4
100.0%

필지수
Real number (ℝ)

HIGH CORRELATION 

Distinct218
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5971.1938
Minimum1
Maximum46840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T23:53:10.312354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.6
Q1291
median3659
Q38135.5
95-th percentile21605
Maximum46840
Range46839
Interquartile range (IQR)7844.5

Descriptive statistics

Standard deviation7703.5615
Coefficient of variation (CV)1.2901208
Kurtosis6.6024405
Mean5971.1938
Median Absolute Deviation (MAD)3539
Skewness2.2487509
Sum1355461
Variance59344860
MonotonicityNot monotonic
2023-12-12T23:53:10.509967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
1.8%
2 3
 
1.3%
6 2
 
0.9%
36 2
 
0.9%
33 2
 
0.9%
952 2
 
0.9%
1188 1
 
0.4%
38108 1
 
0.4%
25149 1
 
0.4%
7775 1
 
0.4%
Other values (208) 208
91.6%
ValueCountFrequency (%)
1 4
1.8%
2 3
1.3%
3 1
 
0.4%
5 1
 
0.4%
6 2
0.9%
9 1
 
0.4%
11 1
 
0.4%
12 1
 
0.4%
13 1
 
0.4%
14 1
 
0.4%
ValueCountFrequency (%)
46840 1
0.4%
42263 1
0.4%
38108 1
0.4%
31138 1
0.4%
30333 1
0.4%
25188 1
0.4%
25161 1
0.4%
25149 1
0.4%
23844 1
0.4%
22869 1
0.4%

면적(ha)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1397.4339
Minimum0.0072
Maximum12293.867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T23:53:10.707551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0072
5-th percentile1.1462829
Q163.546723
median737.15478
Q31941.4867
95-th percentile4893.1965
Maximum12293.867
Range12293.86
Interquartile range (IQR)1877.94

Descriptive statistics

Standard deviation1857.7897
Coefficient of variation (CV)1.3294295
Kurtosis7.4295521
Mean1397.4339
Median Absolute Deviation (MAD)718.08538
Skewness2.3032146
Sum317217.48
Variance3451382.6
MonotonicityNot monotonic
2023-12-12T23:53:10.911056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.094274 1
 
0.4%
2227.605075 1
 
0.4%
4802.834497 1
 
0.4%
921.307302 1
 
0.4%
4993.008721 1
 
0.4%
3432.991022 1
 
0.4%
1693.783483 1
 
0.4%
3988.508078 1
 
0.4%
3052.458701 1
 
0.4%
1971.872174 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
0.0072 1
0.4%
0.049433 1
0.4%
0.0767 1
0.4%
0.164 1
0.4%
0.2485 1
0.4%
0.2615 1
0.4%
0.365555 1
0.4%
0.37285 1
0.4%
0.638513 1
0.4%
0.7391 1
0.4%
ValueCountFrequency (%)
12293.86727 1
0.4%
10005.5916 1
0.4%
8179.64541 1
0.4%
8045.174551 1
0.4%
6220.008339 1
0.4%
5674.416825 1
0.4%
5624.868114 1
0.4%
5591.598265 1
0.4%
5237.935564 1
0.4%
5069.368333 1
0.4%

Interactions

2023-12-12T23:53:08.375097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:07.649961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:07.999603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:08.482329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:07.776688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:08.138752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:08.594950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:07.877529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:53:08.259403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:53:11.005944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시도필지수면적(ha)
시도코드1.0001.0000.3490.243
시도1.0001.0000.5070.433
필지수0.3490.5071.0000.960
면적(ha)0.2430.4330.9601.000
2023-12-12T23:53:11.100894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드필지수면적(ha)시도
시도코드1.0000.6530.6130.979
필지수0.6531.0000.9790.224
면적(ha)0.6130.9791.0000.184
시도0.9790.2240.1841.000

Missing values

2023-12-12T23:53:08.753635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:53:08.872549image/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

시도코드시도시군구필지수면적(ha)
011서울특별시강남구283.094274
111서울특별시강동구365.510952
211서울특별시강서구10223.727478
311서울특별시구로구121.543865
411서울특별시노원구20.931552
511서울특별시도봉구541.535238
611서울특별시서초구10912.0884
711서울특별시송파구212.722384
811서울특별시양천구61.587225
911서울특별시은평구131.41414
시도코드시도시군구필지수면적(ha)
21748경상남도창원시성산구637.385035
21848경상남도창원시의창구2802743.100018
21948경상남도창원시진해구459455.824774
22048경상남도통영시1155122.427702
22148경상남도하동군73111106.490606
22248경상남도함안군4450792.022933
22348경상남도함양군6188974.917696
22448경상남도합천군72991127.443257
22550제주특별자치도서귀포시117543809.088633
22650제주특별자치도제주시128375674.416825