Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory498.0 KiB
Average record size in memory51.0 B

Variable types

Text1
Categorical1
Numeric3

Dataset

Description국토교통부장관이 매년 공시하는 표준지 공시지가를 기준으로 하동군수가 조사한 개별토지의 특성과 비교표준지의 특성을 비교하여, 국토교통부장관이 개발 공급한 토지가격비준표상의 토지특성차이에 따른 가격배율을 산출하고, 이를 표준지 공시지가에 곱하여 산정한 후 감정평가업자의 검증을 받아 토지소유자 등의 의견 수렴과 하동군부동산평가위원회 심의 등의 절차를 거쳐 하동군수가 결정ㆍ공시하는 개별토지의 단위면적당 가격(원/㎡).
Author경상남도 하동군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15002805

Alerts

전년지가 is highly overall correlated with 결정지가High correlation
결정지가 is highly overall correlated with 전년지가High correlation
면적 is highly skewed (γ1 = 37.34778031)Skewed
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:33:15.068404
Analysis finished2023-12-10 23:33:17.101436
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T08:33:17.425004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length16.4268
Min length12

Characters and Unicode

Total characters164268
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row하동군 하동읍 읍내리 1015
2nd row하동군 하동읍 읍내리 1048-3
3rd row하동군 화개면 범왕리 313
4th row하동군 악양면 정서리 245-5
5th row하동군 악양면 평사리 81
ValueCountFrequency (%)
하동군 10000
24.3%
악양면 3144
 
7.7%
화개면 2579
 
6.3%
하동읍 2514
 
6.1%
적량면 1763
 
4.3%
산 1072
 
2.6%
읍내리 487
 
1.2%
우계리 479
 
1.2%
화심리 432
 
1.1%
동산리 382
 
0.9%
Other values (6197) 18220
44.4%
2023-12-11T08:33:18.005307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31072
18.9%
동 13541
 
8.2%
하 12514
 
7.6%
군 10000
 
6.1%
리 10000
 
6.1%
1 7615
 
4.6%
면 7486
 
4.6%
- 6090
 
3.7%
2 4710
 
2.9%
3 3997
 
2.4%
Other values (63) 57243
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89930
54.7%
Decimal Number 37176
22.6%
Space Separator 31072
 
18.9%
Dash Punctuation 6090
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 13541
15.1%
하 12514
13.9%
군 10000
11.1%
리 10000
11.1%
면 7486
 
8.3%
악 3144
 
3.5%
양 3144
 
3.5%
화 3011
 
3.3%
읍 3001
 
3.3%
개 2579
 
2.9%
Other values (51) 21510
23.9%
Decimal Number
ValueCountFrequency (%)
1 7615
20.5%
2 4710
12.7%
3 3997
10.8%
4 3591
9.7%
5 3349
9.0%
6 3077
8.3%
7 2920
 
7.9%
8 2830
 
7.6%
0 2570
 
6.9%
9 2517
 
6.8%
Space Separator
ValueCountFrequency (%)
31072
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6090
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89930
54.7%
Common 74338
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 13541
15.1%
하 12514
13.9%
군 10000
11.1%
리 10000
11.1%
면 7486
 
8.3%
악 3144
 
3.5%
양 3144
 
3.5%
화 3011
 
3.3%
읍 3001
 
3.3%
개 2579
 
2.9%
Other values (51) 21510
23.9%
Common
ValueCountFrequency (%)
31072
41.8%
1 7615
 
10.2%
- 6090
 
8.2%
2 4710
 
6.3%
3 3997
 
5.4%
4 3591
 
4.8%
5 3349
 
4.5%
6 3077
 
4.1%
7 2920
 
3.9%
8 2830
 
3.8%
Other values (2) 5087
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89930
54.7%
ASCII 74338
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31072
41.8%
1 7615
 
10.2%
- 6090
 
8.2%
2 4710
 
6.3%
3 3997
 
5.4%
4 3591
 
4.8%
5 3349
 
4.5%
6 3077
 
4.1%
7 2920
 
3.9%
8 2830
 
3.8%
Other values (2) 5087
 
6.8%
Hangul
ValueCountFrequency (%)
동 13541
15.1%
하 12514
13.9%
군 10000
11.1%
리 10000
11.1%
면 7486
 
8.3%
악 3144
 
3.5%
양 3144
 
3.5%
화 3011
 
3.3%
읍 3001
 
3.3%
개 2579
 
2.9%
Other values (51) 21510
23.9%

지목
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
답
2436 
임
1983 
도
1827 
전
1503 
대
1265 
Other values (20)
986 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row대
2nd row대
3rd row임
4th row답
5th row전

Common Values

ValueCountFrequency (%)
답 2436
24.4%
임 1983
19.8%
도 1827
18.3%
전 1503
15.0%
대 1265
12.7%
구 332
 
3.3%
천 139
 
1.4%
묘 93
 
0.9%
제 88
 
0.9%
잡 69
 
0.7%
Other values (15) 265
 
2.6%

Length

2023-12-11T08:33:18.195839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
답 2436
24.4%
임 1983
19.8%
도 1827
18.3%
전 1503
15.0%
대 1265
12.7%
구 332
 
3.3%
천 139
 
1.4%
묘 93
 
0.9%
제 88
 
0.9%
잡 69
 
0.7%
Other values (15) 265
 
2.6%

면적
Real number (ℝ)

SKEWED 

Distinct2488
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1873.6874
Minimum1
Maximum826560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:33:18.385320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q194
median304
Q3889
95-th percentile5239.08
Maximum826560
Range826559
Interquartile range (IQR)795

Descriptive statistics

Standard deviation12062.733
Coefficient of variation (CV)6.4379646
Kurtosis2255.6351
Mean1873.6874
Median Absolute Deviation (MAD)258
Skewness37.34778
Sum18736874
Variance1.4550954 × 108
MonotonicityNot monotonic
2023-12-11T08:33:18.564491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 69
 
0.7%
7.0 69
 
0.7%
3.0 67
 
0.7%
20.0 59
 
0.6%
33.0 55
 
0.5%
66.0 55
 
0.5%
99.0 53
 
0.5%
26.0 53
 
0.5%
23.0 53
 
0.5%
36.0 53
 
0.5%
Other values (2478) 9414
94.1%
ValueCountFrequency (%)
1.0 16
 
0.2%
2.0 20
 
0.2%
3.0 67
0.7%
3.3 1
 
< 0.1%
3.8 1
 
< 0.1%
4.0 36
0.4%
4.5 1
 
< 0.1%
5.0 42
0.4%
6.0 26
 
0.3%
7.0 69
0.7%
ValueCountFrequency (%)
826560.0 1
< 0.1%
227096.0 1
< 0.1%
218381.0 1
< 0.1%
218182.0 1
< 0.1%
196364.0 1
< 0.1%
179504.0 1
< 0.1%
169984.0 1
< 0.1%
168985.0 1
< 0.1%
155064.0 1
< 0.1%
150149.0 1
< 0.1%

전년지가
Real number (ℝ)

HIGH CORRELATION 

Distinct2258
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38206.956
Minimum0
Maximum1667000
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:33:18.724984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519
Q13300
median10300
Q328000
95-th percentile147695
Maximum1667000
Range1667000
Interquartile range (IQR)24700

Descriptive statistics

Standard deviation112130.76
Coefficient of variation (CV)2.9348257
Kurtosis69.03794
Mean38206.956
Median Absolute Deviation (MAD)8860
Skewness7.4678646
Sum3.8206956 × 108
Variance1.2573307 × 1010
MonotonicityNot monotonic
2023-12-11T08:33:18.906244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4290 94
 
0.9%
5110 82
 
0.8%
6760 64
 
0.6%
6270 62
 
0.6%
4450 61
 
0.6%
5940 59
 
0.6%
13000 54
 
0.5%
10200 48
 
0.5%
10500 43
 
0.4%
3460 41
 
0.4%
Other values (2248) 9392
93.9%
ValueCountFrequency (%)
0 29
0.3%
110 4
 
< 0.1%
113 4
 
< 0.1%
117 1
 
< 0.1%
118 5
 
0.1%
127 7
 
0.1%
143 2
 
< 0.1%
176 1
 
< 0.1%
181 2
 
< 0.1%
184 1
 
< 0.1%
ValueCountFrequency (%)
1667000 1
 
< 0.1%
1489000 2
< 0.1%
1435000 4
< 0.1%
1374000 1
 
< 0.1%
1367000 1
 
< 0.1%
1361000 1
 
< 0.1%
1352000 1
 
< 0.1%
1349000 1
 
< 0.1%
1334000 1
 
< 0.1%
1325000 1
 
< 0.1%

결정지가
Real number (ℝ)

HIGH CORRELATION 

Distinct2306
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35982.142
Minimum101
Maximum1562000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:33:19.133765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile501
Q13087.5
median9700
Q325800
95-th percentile139600
Maximum1562000
Range1561899
Interquartile range (IQR)22712.5

Descriptive statistics

Standard deviation107929.96
Coefficient of variation (CV)2.9995425
Kurtosis71.417924
Mean35982.142
Median Absolute Deviation (MAD)8210
Skewness7.6234159
Sum3.5982142 × 108
Variance1.1648877 × 1010
MonotonicityNot monotonic
2023-12-11T08:33:19.311580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3960 68
 
0.7%
4710 60
 
0.6%
4120 54
 
0.5%
12000 51
 
0.5%
5800 49
 
0.5%
6270 43
 
0.4%
5340 43
 
0.4%
12500 39
 
0.4%
11000 38
 
0.4%
5510 37
 
0.4%
Other values (2296) 9518
95.2%
ValueCountFrequency (%)
101 4
< 0.1%
104 4
< 0.1%
108 1
 
< 0.1%
109 5
0.1%
117 7
0.1%
132 2
 
< 0.1%
162 1
 
< 0.1%
167 2
 
< 0.1%
170 1
 
< 0.1%
180 2
 
< 0.1%
ValueCountFrequency (%)
1562000 1
 
< 0.1%
1457000 2
< 0.1%
1334000 1
 
< 0.1%
1332000 4
< 0.1%
1324000 1
 
< 0.1%
1321000 1
 
< 0.1%
1308000 1
 
< 0.1%
1296000 1
 
< 0.1%
1287000 2
< 0.1%
1275000 1
 
< 0.1%

Interactions

2023-12-11T08:33:16.495190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:15.785591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:16.122787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:16.600310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:15.900540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:16.245488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:16.742584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:16.006700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:16.361898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:33:19.414657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지목면적전년지가결정지가
지목1.0000.0460.3620.373
면적0.0461.0000.0000.000
전년지가0.3620.0001.0000.988
결정지가0.3730.0000.9881.000
2023-12-11T08:33:19.541679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적전년지가결정지가지목
면적1.000-0.121-0.1250.025
전년지가-0.1211.0000.9910.136
결정지가-0.1250.9911.0000.140
지목0.0250.1360.1401.000

Missing values

2023-12-11T08:33:16.941215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:33:17.055949image/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

소재지지목면적전년지가결정지가
3085하동군 하동읍 읍내리 1015대463.0218300199400
3169하동군 하동읍 읍내리 1048-3대49.0150800139900
41780하동군 화개면 범왕리 313임281.0490411
68693하동군 악양면 정서리 245-5답16.02420022200
75247하동군 악양면 평사리 81전1689.02870015600
88807하동군 적량면 서리 545-3임296.019901870
21869하동군 하동읍 두곡리 684-1잡641.07760070000
26894하동군 화개면 부춘리 산 194-5도143.029702730
64343하동군 악양면 등촌리 산 80-1임17452.0623577
27187하동군 화개면 덕은리 140대635.05850054200
소재지지목면적전년지가결정지가
12526하동군 하동읍 목도리 501임119.065606050
52618하동군 악양면 신대리 384임585.039903690
75240하동군 악양면 평사리 75전1172.02870015600
66094하동군 악양면 매계리 653답675.01980018300
92620하동군 적량면 동산리 716도285.064305940
5583하동군 하동읍 광평리 429-38도19.0157400144900
31718하동군 화개면 삼신리 42-2답829.04760045100
82776하동군 적량면 우계리 1324-4도2107.030502820
57553하동군 악양면 신흥리 848답1881.01460013600
26089하동군 화개면 부춘리 1002-16전188.01080010000