Overview

Dataset statistics

Number of variables6
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory54.6 B

Variable types

Numeric5
Categorical1

Dataset

Description회계연도 별 국유지의 현황정보 데이터를 제공하는 자료로, 구분(지역), 필지 수, 면적, 비율, 금액(억원) 정보를 제공합니다.
Author기획재정부
URLhttps://www.data.go.kr/data/15087533/fileData.do

Alerts

필지수 is highly overall correlated with 면적(제곱킬로미터) and 3 other fieldsHigh correlation
면적(제곱킬로미터) is highly overall correlated with 필지수 and 2 other fieldsHigh correlation
면적비율(퍼센트) is highly overall correlated with 필지수 and 2 other fieldsHigh correlation
금액(억원) is highly overall correlated with 필지수 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 필지수 and 3 other fieldsHigh correlation
필지수 has unique valuesUnique
금액(억원) has unique valuesUnique
면적비율(퍼센트) has 8 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-12 00:36:39.186413
Analysis finished2023-12-12 00:36:42.417814
Duration3.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Real number (ℝ)

Distinct8
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.5
Minimum2014
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T09:36:42.498647image/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 deviation2.3057441
Coefficient of variation (CV)0.0011428719
Kurtosis-1.2401884
Mean2017.5
Median Absolute Deviation (MAD)2
Skewness0
Sum161400
Variance5.3164557
MonotonicityDecreasing
2023-12-12T09:36:42.668500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2021 10
12.5%
2020 10
12.5%
2019 10
12.5%
2018 10
12.5%
2017 10
12.5%
2016 10
12.5%
2015 10
12.5%
2014 10
12.5%
ValueCountFrequency (%)
2014 10
12.5%
2015 10
12.5%
2016 10
12.5%
2017 10
12.5%
2018 10
12.5%
2019 10
12.5%
2020 10
12.5%
2021 10
12.5%
ValueCountFrequency (%)
2021 10
12.5%
2020 10
12.5%
2019 10
12.5%
2018 10
12.5%
2017 10
12.5%
2016 10
12.5%
2015 10
12.5%
2014 10
12.5%

구분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
강원도
8 
경상북도
8 
경기도
8 
전라남도
8 
전라북도
8 
Other values (5)
40 

Length

Max length7
Median length4
Mean length3.9
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row경상북도
3rd row경기도
4th row전라남도
5th row전라북도

Common Values

ValueCountFrequency (%)
강원도 8
10.0%
경상북도 8
10.0%
경기도 8
10.0%
전라남도 8
10.0%
전라북도 8
10.0%
경상남도 8
10.0%
충청북도 8
10.0%
충청남도 8
10.0%
세종특별자치시 8
10.0%
기타 8
10.0%

Length

2023-12-12T09:36:42.865240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:36:43.074712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 8
10.0%
경상북도 8
10.0%
경기도 8
10.0%
전라남도 8
10.0%
전라북도 8
10.0%
경상남도 8
10.0%
충청북도 8
10.0%
충청남도 8
10.0%
세종특별자치시 8
10.0%
기타 8
10.0%

필지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567856.1
Minimum28516
Maximum920458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T09:36:43.287740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28516
5-th percentile30178.45
Q1493332.75
median576102.5
Q3766760.5
95-th percentile899683.25
Maximum920458
Range891942
Interquartile range (IQR)273427.75

Descriptive statistics

Standard deviation238597.86
Coefficient of variation (CV)0.42017311
Kurtosis0.35700073
Mean567856.1
Median Absolute Deviation (MAD)125590
Skewness-0.80571228
Sum45428488
Variance5.692894 × 1010
MonotonicityNot monotonic
2023-12-12T09:36:43.461086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
560630 1
 
1.2%
897571 1
 
1.2%
530988 1
 
1.2%
29825 1
 
1.2%
493699 1
 
1.2%
348845 1
 
1.2%
649173 1
 
1.2%
623014 1
 
1.2%
812952 1
 
1.2%
764187 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
28516 1
1.2%
28586 1
1.2%
29825 1
1.2%
29997 1
1.2%
30188 1
1.2%
31346 1
1.2%
32211 1
1.2%
32409 1
1.2%
335823 1
1.2%
341177 1
1.2%
ValueCountFrequency (%)
920458 1
1.2%
915329 1
1.2%
905778 1
1.2%
901341 1
1.2%
899596 1
1.2%
897571 1
1.2%
868600 1
1.2%
847741 1
1.2%
840024 1
1.2%
832764 1
1.2%

면적(제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2499.7875
Minimum77
Maximum8999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T09:36:43.627081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile80
Q11250.75
median1739.5
Q32137.25
95-th percentile8944.85
Maximum8999
Range8922
Interquartile range (IQR)886.5

Descriptive statistics

Standard deviation2373.2882
Coefficient of variation (CV)0.94939598
Kurtosis3.0031962
Mean2499.7875
Median Absolute Deviation (MAD)450.5
Skewness1.965
Sum199983
Variance5632496.9
MonotonicityNot monotonic
2023-12-12T09:36:43.770108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1229 2
 
2.5%
80 2
 
2.5%
8987 1
 
1.2%
8903 1
 
1.2%
79 1
 
1.2%
1208 1
 
1.2%
1658 1
 
1.2%
1679 1
 
1.2%
1806 1
 
1.2%
2021 1
 
1.2%
Other values (68) 68
85.0%
ValueCountFrequency (%)
77 1
1.2%
78 1
1.2%
79 1
1.2%
80 2
2.5%
81 1
1.2%
82 1
1.2%
101 1
1.2%
1182 1
1.2%
1194 1
1.2%
1197 1
1.2%
ValueCountFrequency (%)
8999 1
1.2%
8987 1
1.2%
8978 1
1.2%
8961 1
1.2%
8944 1
1.2%
8923 1
1.2%
8903 1
1.2%
8886 1
1.2%
4225 1
1.2%
4205 1
1.2%

면적비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.0875
Minimum0
Maximum36
Zeros8
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T09:36:43.916017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median7
Q39
95-th percentile36
Maximum36
Range36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.5495957
Coefficient of variation (CV)0.94667616
Kurtosis2.8853657
Mean10.0875
Median Absolute Deviation (MAD)2
Skewness1.9199715
Sum807
Variance91.194778
MonotonicityNot monotonic
2023-12-12T09:36:44.052199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 24
30.0%
5 16
20.0%
17 8
 
10.0%
9 8
 
10.0%
8 8
 
10.0%
0 8
 
10.0%
36 7
 
8.8%
35 1
 
1.2%
ValueCountFrequency (%)
0 8
 
10.0%
5 16
20.0%
7 24
30.0%
8 8
 
10.0%
9 8
 
10.0%
17 8
 
10.0%
35 1
 
1.2%
36 7
 
8.8%
ValueCountFrequency (%)
36 7
 
8.8%
35 1
 
1.2%
17 8
 
10.0%
9 8
 
10.0%
8 8
 
10.0%
7 24
30.0%
5 16
20.0%
0 8
 
10.0%

금액(억원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean489794.81
Minimum21557
Maximum2912471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T09:36:44.218406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21557
5-th percentile49886.95
Q1144127.75
median212436
Q3403569.5
95-th percentile2065163.8
Maximum2912471
Range2890914
Interquartile range (IQR)259441.75

Descriptive statistics

Standard deviation651080.46
Coefficient of variation (CV)1.3292923
Kurtosis3.5688544
Mean489794.81
Median Absolute Deviation (MAD)107324
Skewness2.1010097
Sum39183585
Variance4.2390577 × 1011
MonotonicityNot monotonic
2023-12-12T09:36:44.396670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239695 1
 
1.2%
397781 1
 
1.2%
2053164 1
 
1.2%
47022 1
 
1.2%
146864 1
 
1.2%
92561 1
 
1.2%
307831 1
 
1.2%
232806 1
 
1.2%
171522 1
 
1.2%
1040689 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
21557 1
1.2%
24425 1
1.2%
47022 1
1.2%
48670 1
1.2%
49951 1
1.2%
51787 1
1.2%
53320 1
1.2%
76251 1
1.2%
92561 1
1.2%
92813 1
1.2%
ValueCountFrequency (%)
2912471 1
1.2%
2412767 1
1.2%
2191165 1
1.2%
2073215 1
1.2%
2064740 1
1.2%
2053164 1
1.2%
2046119 1
1.2%
2008187 1
1.2%
1327895 1
1.2%
1095815 1
1.2%

Interactions

2023-12-12T09:36:41.600883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.391739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.150340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.581346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.061666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.696428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.738919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.237566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.680827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.161005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.802902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.834556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.315207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.826544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.268022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.909819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.934966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.397269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.895448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.380386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:42.053808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.049634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.489613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.978903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.498693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:36:44.524330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도구분필지수면적(제곱킬로미터)면적비율(퍼센트)금액(억원)
회계연도1.0000.0000.0000.0000.0000.000
구분0.0001.0000.9511.0001.0000.794
필지수0.0000.9511.0000.8920.8920.607
면적(제곱킬로미터)0.0001.0000.8921.0001.0000.698
면적비율(퍼센트)0.0001.0000.8921.0001.0000.698
금액(억원)0.0000.7940.6070.6980.6981.000
2023-12-12T09:36:44.664765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도필지수면적(제곱킬로미터)면적비율(퍼센트)금액(억원)구분
회계연도1.0000.1230.113-0.0030.1230.000
필지수0.1231.0000.7310.6930.6120.855
면적(제곱킬로미터)0.1130.7311.0000.9790.3700.966
면적비율(퍼센트)-0.0030.6930.9791.0000.3000.966
금액(억원)0.1230.6120.3700.3001.0000.517
구분0.0000.8550.9660.9660.5171.000

Missing values

2023-12-12T09:36:42.194082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:36:42.347440image/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)
02021강원도560630898735239695
12021경상북도920458422517653989
22021경기도819998216491327895
32021전라남도84002421098166702
42021전라북도63992318537270611
52021경상남도65376017177360930
62021충청북도37754417057121326
72021충청남도50841912355171155
82021세종특별자치시32409101076251
92021기타530105125952912471
회계연도구분필지수면적(제곱킬로미터)면적비율(퍼센트)금액(억원)
702014강원도509019888636138741
712014경상북도847741407917388932
722014경기도72885720979947266
732014전라남도78669919638136456
742014전라북도59157517627207779
752014경상남도60747116267277519
762014충청북도3358231615793130
772014충청남도47767711825155125
782014세종특별자치시2851677021557
792014기타504569122952008187