Overview

Dataset statistics

Number of variables6
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory55.8 B

Variable types

Numeric5
Categorical1

Dataset

Description회계연도 별 중앙관서별 국유지 현황 데이터를 제공하는 자료로 필지 수, 면적, 비율, 금액에 관한 정보를 제공합니다. 산림청, 국토부, 농림부, 기획재정부, 국방부 5개 부처 위주 정보를 제공합니다.
Author기획재정부
URLhttps://www.data.go.kr/data/15087534/fileData.do

Alerts

필지수 is highly overall correlated with 구분High correlation
면적(제곱킬로미터) is highly overall correlated with 면적비율(퍼센트) and 1 other fieldsHigh correlation
면적비율(퍼센트) is highly overall correlated with 면적(제곱킬로미터) and 1 other fieldsHigh correlation
금액(억원) is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 필지수 and 3 other fieldsHigh correlation
필지수 has unique valuesUnique
금액(억원) has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:26:03.469591
Analysis finished2023-12-13 00:26:05.810522
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Real number (ℝ)

Distinct8
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.5
Minimum2014
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T09:26:05.855923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12015.75
median2017.5
Q32019.25
95-th percentile2021
Maximum2021
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.315535
Coefficient of variation (CV)0.0011477249
Kurtosis-1.2412238
Mean2017.5
Median Absolute Deviation (MAD)2
Skewness0
Sum96840
Variance5.3617021
MonotonicityDecreasing
2023-12-13T09:26:05.940824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2021 6
12.5%
2020 6
12.5%
2019 6
12.5%
2018 6
12.5%
2017 6
12.5%
2016 6
12.5%
2015 6
12.5%
2014 6
12.5%
ValueCountFrequency (%)
2014 6
12.5%
2015 6
12.5%
2016 6
12.5%
2017 6
12.5%
2018 6
12.5%
2019 6
12.5%
2020 6
12.5%
2021 6
12.5%
ValueCountFrequency (%)
2021 6
12.5%
2020 6
12.5%
2019 6
12.5%
2018 6
12.5%
2017 6
12.5%
2016 6
12.5%
2015 6
12.5%
2014 6
12.5%

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size516.0 B
산림청
8 
국토교통부
8 
농림축산식품부
8 
국방부
8 
기획재정부
8 

Length

Max length7
Median length5
Mean length4.1666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row산림청
2nd row국토교통부
3rd row농림축산식품부
4th row국방부
5th row기획재정부

Common Values

ValueCountFrequency (%)
산림청 8
16.7%
국토교통부 8
16.7%
농림축산식품부 8
16.7%
국방부 8
16.7%
기획재정부 8
16.7%
기타 8
16.7%

Length

2023-12-13T09:26:06.041442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:26:06.123592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산림청 8
16.7%
국토교통부 8
16.7%
농림축산식품부 8
16.7%
국방부 8
16.7%
기획재정부 8
16.7%
기타 8
16.7%

필지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean946426.83
Minimum78764
Maximum3551833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T09:26:06.226929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78764
5-th percentile82322.25
Q1186417
median465471
Q31109255.8
95-th percentile3399382.1
Maximum3551833
Range3473069
Interquartile range (IQR)922838.75

Descriptive statistics

Standard deviation1164553.3
Coefficient of variation (CV)1.2304736
Kurtosis0.76523005
Mean946426.83
Median Absolute Deviation (MAD)358741
Skewness1.5034395
Sum45428488
Variance1.3561843 × 1012
MonotonicityNot monotonic
2023-12-13T09:26:06.336875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
109179 1
 
2.1%
3551833 1
 
2.1%
212163 1
 
2.1%
631913 1
 
2.1%
83788 1
 
2.1%
104528 1
 
2.1%
3543146 1
 
2.1%
1107609 1
 
2.1%
212881 1
 
2.1%
636691 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
78764 1
2.1%
81426 1
2.1%
81533 1
2.1%
83788 1
2.1%
102028 1
2.1%
102991 1
2.1%
104528 1
2.1%
106094 1
2.1%
107366 1
2.1%
108067 1
2.1%
ValueCountFrequency (%)
3551833 1
2.1%
3543146 1
2.1%
3411706 1
2.1%
3376495 1
2.1%
3372098 1
2.1%
3345422 1
2.1%
3318565 1
2.1%
3281282 1
2.1%
1125108 1
2.1%
1121911 1
2.1%

면적(제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4166.4792
Minimum445
Maximum15171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T09:26:06.441636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum445
5-th percentile452.35
Q11028
median1738.5
Q35132.25
95-th percentile15037.35
Maximum15171
Range14726
Interquartile range (IQR)4104.25

Descriptive statistics

Standard deviation5114.7174
Coefficient of variation (CV)1.2275874
Kurtosis0.71093256
Mean4166.4792
Median Absolute Deviation (MAD)1191
Skewness1.5038007
Sum199991
Variance26160334
MonotonicityNot monotonic
2023-12-13T09:26:06.552197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1339 2
 
4.2%
15171 1
 
2.1%
5424 1
 
2.1%
449 1
 
2.1%
635 1
 
2.1%
14831 1
 
2.1%
5538 1
 
2.1%
2119 1
 
2.1%
1358 1
 
2.1%
464 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
445 1
2.1%
449 1
2.1%
452 1
2.1%
453 1
2.1%
459 1
2.1%
464 1
2.1%
480 1
2.1%
489 1
2.1%
606 1
2.1%
621 1
2.1%
ValueCountFrequency (%)
15171 1
2.1%
15130 1
2.1%
15079 1
2.1%
14960 1
2.1%
14922 1
2.1%
14831 1
2.1%
14737 1
2.1%
14627 1
2.1%
5538 1
2.1%
5526 1
2.1%

면적비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.8125
Minimum2
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T09:26:06.639696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14.5
median7
Q320.5
95-th percentile60
Maximum60
Range58
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.501849
Coefficient of variation (CV)1.2194408
Kurtosis0.71556201
Mean16.8125
Median Absolute Deviation (MAD)5
Skewness1.507319
Sum807
Variance420.3258
MonotonicityNot monotonic
2023-12-13T09:26:06.715912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 9
18.8%
60 8
16.7%
5 8
16.7%
20 4
8.3%
8 4
8.3%
6 4
8.3%
9 4
8.3%
22 4
8.3%
3 3
 
6.2%
ValueCountFrequency (%)
2 9
18.8%
3 3
 
6.2%
5 8
16.7%
6 4
8.3%
8 4
8.3%
9 4
8.3%
20 4
8.3%
22 4
8.3%
60 8
16.7%
ValueCountFrequency (%)
60 8
16.7%
22 4
8.3%
20 4
8.3%
9 4
8.3%
8 4
8.3%
6 4
8.3%
5 8
16.7%
3 3
 
6.2%
2 9
18.8%

금액(억원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean816324.65
Minimum139191
Maximum3010668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T09:26:06.810463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum139191
5-th percentile165105.1
Q1234023.5
median530121.5
Q3925826.5
95-th percentile2355381.9
Maximum3010668
Range2871477
Interquartile range (IQR)691803

Descriptive statistics

Standard deviation785634.11
Coefficient of variation (CV)0.96240401
Kurtosis0.86199612
Mean816324.65
Median Absolute Deviation (MAD)319701.5
Skewness1.4368905
Sum39183583
Variance6.1722095 × 1011
MonotonicityNot monotonic
2023-12-13T09:26:06.916713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
218313 1
 
2.1%
2306326 1
 
2.1%
623844 1
 
2.1%
218364 1
 
2.1%
902303 1
 
2.1%
164354 1
 
2.1%
2325454 1
 
2.1%
428181 1
 
2.1%
627198 1
 
2.1%
239288 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
139191 1
2.1%
142262 1
2.1%
164354 1
2.1%
166500 1
2.1%
168709 1
2.1%
174423 1
2.1%
182122 1
2.1%
199206 1
2.1%
202527 1
2.1%
218313 1
2.1%
ValueCountFrequency (%)
3010668 1
2.1%
2517543 1
2.1%
2371497 1
2.1%
2325454 1
2.1%
2313947 1
2.1%
2306326 1
2.1%
2298658 1
2.1%
2235831 1
2.1%
1309351 1
2.1%
1102883 1
2.1%

Interactions

2023-12-13T09:26:05.089992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:03.644064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.025348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.372419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.758838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:05.157692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:03.714944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.106020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.451212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.827775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:05.466259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:03.784699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.173241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.541151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.897049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:05.531746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:03.862994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.241758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.624045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.963552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:05.597542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:03.944002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.305050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:04.692238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:26:05.025005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:26:06.985327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도구분필지수면적(제곱킬로미터)면적비율(퍼센트)금액(억원)
회계연도1.0000.0000.0000.0000.0000.000
구분0.0001.0000.9031.0001.0000.814
필지수0.0000.9031.0000.8450.8470.664
면적(제곱킬로미터)0.0001.0000.8451.0001.0000.907
면적비율(퍼센트)0.0001.0000.8471.0001.0000.903
금액(억원)0.0000.8140.6640.9070.9031.000
2023-12-13T09:26:07.062147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도필지수면적(제곱킬로미터)면적비율(퍼센트)금액(억원)구분
회계연도1.0000.1640.040-0.0050.1360.000
필지수0.1641.0000.0860.0900.4270.839
면적(제곱킬로미터)0.0400.0861.0000.989-0.0530.977
면적비율(퍼센트)-0.0050.0900.9891.000-0.0670.977
금액(억원)0.1360.427-0.053-0.0671.0000.616
구분0.0000.8390.9770.9770.6161.000

Missing values

2023-12-13T09:26:05.686480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:26:05.776340image/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

회계연도구분필지수면적(제곱킬로미터)면적비율(퍼센트)금액(억원)
02021산림청1091791517160218313
12021국토교통부34117065060203010668
22021농림축산식품부112510821248534723
32021국방부21506413395897499
42021기획재정부7269524892330471
52021기타295261117251309351
62020산림청1089661513060182122
72020국토교통부33720985007202517543
82020농림축산식품부112191121228406928
92020국방부21425213395733523
회계연도구분필지수면적(제곱킬로미터)면적비율(퍼센트)금액(억원)
382015농림축산식품부111424021319446912
392015국방부21564913475528847
402015기획재정부6259924592202527
412015기타815336213793786
422014산림청1020281462760139191
432014국토교통부32812825349222313947
442014농림축산식품부111419621329463850
452014국방부22158513626531396
462014기획재정부6200924452199206
472014기타787646062727102