Overview

Dataset statistics

Number of variables6
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory52.7 B

Variable types

Categorical2
Numeric3
Text1

Dataset

Description2022년 12월 31일 기준의 농지전용현황에 대한 데이터로 농지전용건수(건), 면적(㎡)에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3055540/fileData.do

Alerts

년도 has constant value ""Constant
지역코드 is highly overall correlated with 시도High correlation
건수 is highly overall correlated with 면적High correlation
면적 is highly overall correlated with 건수High correlation
시도 is highly overall correlated with 지역코드High correlation

Reproduction

Analysis started2023-12-12 08:32:55.615381
Analysis finished2023-12-12 08:32:57.440016
Duration1.82 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2022
200 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 200
100.0%

Length

2023-12-12T17:32:57.510022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:32:57.652596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 200
100.0%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4498452.5
Minimum3090000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T17:32:57.806415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3090000
5-th percentile3349500
Q13927500
median4515000
Q35072500
95-th percentile5580500
Maximum6520000
Range3430000
Interquartile range (IQR)1145000

Descriptive statistics

Standard deviation736914.08
Coefficient of variation (CV)0.16381502
Kurtosis-0.59789458
Mean4498452.5
Median Absolute Deviation (MAD)575000
Skewness0.080044925
Sum8.996905 × 108
Variance5.4304237 × 1011
MonotonicityIncreasing
2023-12-12T17:32:57.999121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4490000 3
 
1.5%
3090000 1
 
0.5%
4950000 1
 
0.5%
4850000 1
 
0.5%
4860000 1
 
0.5%
4870000 1
 
0.5%
4880000 1
 
0.5%
4890000 1
 
0.5%
4900000 1
 
0.5%
4910000 1
 
0.5%
Other values (188) 188
94.0%
ValueCountFrequency (%)
3090000 1
0.5%
3110000 1
0.5%
3130000 1
0.5%
3150000 1
0.5%
3210000 1
0.5%
3260000 1
0.5%
3280000 1
0.5%
3300000 1
0.5%
3320000 1
0.5%
3340000 1
0.5%
ValueCountFrequency (%)
6520000 1
0.5%
6510000 1
0.5%
6430000 1
0.5%
5710000 1
0.5%
5700000 1
0.5%
5690000 1
0.5%
5680000 1
0.5%
5670000 1
0.5%
5600000 1
0.5%
5590000 1
0.5%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
경기도
30 
경상북도
23 
전라남도
22 
강원도
18 
경상남도
18 
Other values (12)
89 

Length

Max length7
Median length4
Mean length4.02
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
15.0%
경상북도 23
11.5%
전라남도 22
11.0%
강원도 18
9.0%
경상남도 18
9.0%
충청남도 17
8.5%
전라북도 14
7.0%
충청북도 12
 
6.0%
부산광역시 9
 
4.5%
인천광역시 8
 
4.0%
Other values (7) 29
14.5%

Length

2023-12-12T17:32:58.233589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 30
15.0%
경상북도 23
11.5%
전라남도 22
11.0%
강원도 18
9.0%
경상남도 18
9.0%
충청남도 17
8.5%
전라북도 14
7.0%
충청북도 12
 
6.0%
부산광역시 9
 
4.5%
인천광역시 8
 
4.0%
Other values (7) 29
14.5%

시군구
Text

Distinct185
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T17:32:58.666919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.96
Min length2

Characters and Unicode

Total characters592
Distinct characters125
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

Unique178 ?
Unique (%)89.0%

Sample

1st row도봉구
2nd row은평구
3rd row마포구
4th row강서구
5th row서초구
ValueCountFrequency (%)
서구 5
 
2.5%
북구 4
 
2.0%
동구 4
 
2.0%
중구 3
 
1.5%
강서구 2
 
1.0%
고성군 2
 
1.0%
남구 2
 
1.0%
영광군 1
 
0.5%
진도군 1
 
0.5%
도봉구 1
 
0.5%
Other values (175) 175
87.5%
2023-12-12T17:32:59.305216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 85
 
14.4%
시 80
 
13.5%
구 44
 
7.4%
천 22
 
3.7%
주 20
 
3.4%
양 17
 
2.9%
성 16
 
2.7%
산 14
 
2.4%
안 13
 
2.2%
서 13
 
2.2%
Other values (115) 268
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 592
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 85
 
14.4%
시 80
 
13.5%
구 44
 
7.4%
천 22
 
3.7%
주 20
 
3.4%
양 17
 
2.9%
성 16
 
2.7%
산 14
 
2.4%
안 13
 
2.2%
서 13
 
2.2%
Other values (115) 268
45.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 592
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 85
 
14.4%
시 80
 
13.5%
구 44
 
7.4%
천 22
 
3.7%
주 20
 
3.4%
양 17
 
2.9%
성 16
 
2.7%
산 14
 
2.4%
안 13
 
2.2%
서 13
 
2.2%
Other values (115) 268
45.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 592
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 85
 
14.4%
시 80
 
13.5%
구 44
 
7.4%
천 22
 
3.7%
주 20
 
3.4%
양 17
 
2.9%
성 16
 
2.7%
산 14
 
2.4%
안 13
 
2.2%
서 13
 
2.2%
Other values (115) 268
45.3%

건수
Real number (ℝ)

HIGH CORRELATION 

Distinct177
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean679.645
Minimum1
Maximum10111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T17:32:59.494999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.85
Q1164.25
median447.5
Q3778.25
95-th percentile2345.7
Maximum10111
Range10110
Interquartile range (IQR)614

Descriptive statistics

Standard deviation943.38953
Coefficient of variation (CV)1.3880622
Kurtosis49.977027
Mean679.645
Median Absolute Deviation (MAD)304.5
Skewness5.5938209
Sum135929
Variance889983.81
MonotonicityNot monotonic
2023-12-12T17:32:59.666041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
2.0%
2695 3
 
1.5%
323 2
 
1.0%
70 2
 
1.0%
112 2
 
1.0%
283 2
 
1.0%
176 2
 
1.0%
415 2
 
1.0%
719 2
 
1.0%
291 2
 
1.0%
Other values (167) 177
88.5%
ValueCountFrequency (%)
1 4
2.0%
2 2
1.0%
4 2
1.0%
5 2
1.0%
8 2
1.0%
10 1
 
0.5%
15 1
 
0.5%
17 1
 
0.5%
20 1
 
0.5%
23 1
 
0.5%
ValueCountFrequency (%)
10111 1
 
0.5%
3120 1
 
0.5%
2925 1
 
0.5%
2695 3
1.5%
2568 1
 
0.5%
2442 1
 
0.5%
2436 1
 
0.5%
2435 1
 
0.5%
2341 1
 
0.5%
2336 1
 
0.5%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455401.94
Minimum49
Maximum10825236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T17:32:59.850864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile4276.85
Q193111.522
median252511.15
Q3530026.78
95-th percentile1384885.6
Maximum10825236
Range10825187
Interquartile range (IQR)436915.25

Descriptive statistics

Standard deviation864284.03
Coefficient of variation (CV)1.8978488
Kurtosis104.71742
Mean455401.94
Median Absolute Deviation (MAD)199073.93
Skewness8.9866408
Sum91080389
Variance7.4698689 × 1011
MonotonicityNot monotonic
2023-12-12T17:33:00.008715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2068786.43 3
 
1.5%
5700.0 1
 
0.5%
249514.3 1
 
0.5%
275981.85 1
 
0.5%
362705.0 1
 
0.5%
111214.4 1
 
0.5%
437674.25 1
 
0.5%
286581.5 1
 
0.5%
309096.0 1
 
0.5%
249988.6 1
 
0.5%
Other values (188) 188
94.0%
ValueCountFrequency (%)
49.0 1
0.5%
52.0 1
0.5%
69.0 1
0.5%
103.0 1
0.5%
252.0 1
0.5%
710.0 1
0.5%
973.0 1
0.5%
1768.0 1
0.5%
1832.3 1
0.5%
3875.0 1
0.5%
ValueCountFrequency (%)
10825235.6 1
 
0.5%
2068786.43 3
1.5%
2062871.75 1
 
0.5%
2033692.55 1
 
0.5%
1842845.6 1
 
0.5%
1549215.9 1
 
0.5%
1462169.6 1
 
0.5%
1394512.3 1
 
0.5%
1384378.9 1
 
0.5%
1355655.9 1
 
0.5%

Interactions

2023-12-12T17:32:56.884888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:55.873290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:56.230870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:57.006919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:56.002123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:56.333900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:57.097682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:56.123652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:32:56.758778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:33:00.114630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드시도건수면적
지역코드1.0000.9620.5390.793
시도0.9621.0000.2760.462
건수0.5390.2761.0000.854
면적0.7930.4620.8541.000
2023-12-12T17:33:00.230759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드건수면적시도
지역코드1.0000.4620.4600.810
건수0.4621.0000.9630.140
면적0.4600.9631.0000.272
시도0.8100.1400.2721.000

Missing values

2023-12-12T17:32:57.227128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:32:57.379003image/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

년도지역코드시도시군구건수면적
020223090000서울특별시도봉구105700.0
120223110000서울특별시은평구316834.0
220223130000서울특별시마포구21768.0
320223150000서울특별시강서구1103.0
420223210000서울특별시서초구53875.0
520223260000부산광역시서구84298.0
620223280000부산광역시영도구157716.0
720223300000부산광역시동래구452.0
820223320000부산광역시북구85793.0
920223340000부산광역시사하구4973.0
년도지역코드시도시군구건수면적
19020225590000경기도양주시1271892182.8
19120225600000경기도포천시20321462169.6
19220225670000경상남도창원시29251549215.9
19320225680000충청남도당진시884549623.3
19420225690000세종특별자치시세종시788327207.03
19520225700000경기도여주시13371176999.3
19620225710000충청북도청주시19321394512.3
19720226430000충청북도본청1011110825235.6
19820226510000제주특별자치도제주시23362033692.55
19920226520000제주특별자치도서귀포시14531316241.64