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
면 적 is highly skewed (γ1 = 66.42357011)Skewed
소 재 지 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:33:04.232049
Analysis finished2023-12-10 23:33:05.766304
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소 재 지
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T08:33:06.034328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length16.4558
Min length12

Characters and Unicode

Total characters164558
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 화개면 정금리 산 89
2nd row하동군 악양면 신성리 632-1
3rd row하동군 악양면 중대리 436-4
4th row하동군 화개면 범왕리 45
5th row하동군 악양면 입석리 825-1
ValueCountFrequency (%)
하동군 10000
24.3%
악양면 3164
 
7.7%
화개면 2529
 
6.1%
하동읍 2467
 
6.0%
적량면 1840
 
4.5%
산 1124
 
2.7%
우계리 478
 
1.2%
읍내리 472
 
1.1%
화심리 411
 
1.0%
동산리 404
 
1.0%
Other values (6213) 18235
44.3%
2023-12-11T08:33:06.484556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31124
18.9%
동 13579
 
8.3%
하 12467
 
7.6%
리 10000
 
6.1%
군 10000
 
6.1%
1 7698
 
4.7%
면 7533
 
4.6%
- 6180
 
3.8%
2 4734
 
2.9%
3 4018
 
2.4%
Other values (63) 57225
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89951
54.7%
Decimal Number 37303
22.7%
Space Separator 31124
 
18.9%
Dash Punctuation 6180
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 13579
15.1%
하 12467
13.9%
리 10000
11.1%
군 10000
11.1%
면 7533
 
8.4%
악 3164
 
3.5%
양 3164
 
3.5%
화 2940
 
3.3%
읍 2939
 
3.3%
개 2529
 
2.8%
Other values (51) 21636
24.1%
Decimal Number
ValueCountFrequency (%)
1 7698
20.6%
2 4734
12.7%
3 4018
10.8%
4 3507
9.4%
5 3342
9.0%
6 3107
8.3%
7 2886
 
7.7%
8 2882
 
7.7%
9 2607
 
7.0%
0 2522
 
6.8%
Space Separator
ValueCountFrequency (%)
31124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89951
54.7%
Common 74607
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 13579
15.1%
하 12467
13.9%
리 10000
11.1%
군 10000
11.1%
면 7533
 
8.4%
악 3164
 
3.5%
양 3164
 
3.5%
화 2940
 
3.3%
읍 2939
 
3.3%
개 2529
 
2.8%
Other values (51) 21636
24.1%
Common
ValueCountFrequency (%)
31124
41.7%
1 7698
 
10.3%
- 6180
 
8.3%
2 4734
 
6.3%
3 4018
 
5.4%
4 3507
 
4.7%
5 3342
 
4.5%
6 3107
 
4.2%
7 2886
 
3.9%
8 2882
 
3.9%
Other values (2) 5129
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89951
54.7%
ASCII 74607
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31124
41.7%
1 7698
 
10.3%
- 6180
 
8.3%
2 4734
 
6.3%
3 4018
 
5.4%
4 3507
 
4.7%
5 3342
 
4.5%
6 3107
 
4.2%
7 2886
 
3.9%
8 2882
 
3.9%
Other values (2) 5129
 
6.9%
Hangul
ValueCountFrequency (%)
동 13579
15.1%
하 12467
13.9%
리 10000
11.1%
군 10000
11.1%
면 7533
 
8.4%
악 3164
 
3.5%
양 3164
 
3.5%
화 2940
 
3.3%
읍 2939
 
3.3%
개 2529
 
2.8%
Other values (51) 21636
24.1%

지목
Categorical

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
답
2403 
임
2093 
도
1846 
전
1463 
대
1213 
Other values (19)
982 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
답 2403
24.0%
임 2093
20.9%
도 1846
18.5%
전 1463
14.6%
대 1213
12.1%
구 355
 
3.5%
천 126
 
1.3%
묘 87
 
0.9%
제 85
 
0.9%
과 78
 
0.8%
Other values (14) 251
 
2.5%

Length

2023-12-11T08:33:06.598073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
답 2403
24.0%
임 2093
20.9%
도 1846
18.5%
전 1463
14.6%
대 1213
12.1%
구 355
 
3.5%
천 126
 
1.3%
묘 87
 
0.9%
제 85
 
0.9%
과 78
 
0.8%
Other values (14) 251
 
2.5%

면 적
Real number (ℝ)

SKEWED 

Distinct2476
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2615.1913
Minimum1
Maximum3984788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:33:06.711775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q199
median313.5
Q3916
95-th percentile5256
Maximum3984788
Range3984787
Interquartile range (IQR)817

Descriptive statistics

Standard deviation53652.827
Coefficient of variation (CV)20.515832
Kurtosis4632.2757
Mean2615.1913
Median Absolute Deviation (MAD)267.5
Skewness66.42357
Sum26151913
Variance2.8786258 × 109
MonotonicityNot monotonic
2023-12-11T08:33:06.897857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0 74
 
0.7%
10.0 73
 
0.7%
13.0 67
 
0.7%
60.0 64
 
0.6%
36.0 63
 
0.6%
3.0 63
 
0.6%
26.0 59
 
0.6%
20.0 54
 
0.5%
40.0 53
 
0.5%
17.0 52
 
0.5%
Other values (2466) 9378
93.8%
ValueCountFrequency (%)
1.0 22
 
0.2%
2.0 26
 
0.3%
3.0 63
0.6%
4.0 33
0.3%
4.5 1
 
< 0.1%
5.0 38
0.4%
6.0 28
 
0.3%
7.0 74
0.7%
8.0 33
0.3%
9.0 30
0.3%
ValueCountFrequency (%)
3984788.0 1
< 0.1%
3393418.0 1
< 0.1%
515077.0 1
< 0.1%
457545.0 1
< 0.1%
425844.0 1
< 0.1%
362479.0 1
< 0.1%
230116.0 1
< 0.1%
215207.0 1
< 0.1%
210050.0 1
< 0.1%
190043.0 1
< 0.1%

전년지가
Real number (ℝ)

HIGH CORRELATION 

Distinct2309
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33565.923
Minimum0
Maximum1582000
Zeros60
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:33:07.030825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile465
Q12870
median9240
Q325400
95-th percentile131625
Maximum1582000
Range1582000
Interquartile range (IQR)22530

Descriptive statistics

Standard deviation96993.883
Coefficient of variation (CV)2.8896534
Kurtosis79.644316
Mean33565.923
Median Absolute Deviation (MAD)7920
Skewness7.8837289
Sum3.3565923 × 108
Variance9.4078134 × 109
MonotonicityNot monotonic
2023-12-11T08:33:07.157403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5610 94
 
0.9%
4620 74
 
0.7%
4950 73
 
0.7%
3960 66
 
0.7%
12000 65
 
0.7%
3790 64
 
0.6%
0 60
 
0.6%
5440 44
 
0.4%
14500 44
 
0.4%
5280 43
 
0.4%
Other values (2299) 9373
93.7%
ValueCountFrequency (%)
0 60
0.6%
100 1
 
< 0.1%
102 4
 
< 0.1%
105 7
 
0.1%
108 1
 
< 0.1%
110 8
 
0.1%
120 8
 
0.1%
130 1
 
< 0.1%
140 2
 
< 0.1%
148 1
 
< 0.1%
ValueCountFrequency (%)
1582000 1
< 0.1%
1503000 1
< 0.1%
1463000 1
< 0.1%
1460000 2
< 0.1%
1293000 1
< 0.1%
1277000 1
< 0.1%
1212000 1
< 0.1%
1203000 1
< 0.1%
1192000 1
< 0.1%
1185000 1
< 0.1%

결정지가
Real number (ℝ)

HIGH CORRELATION 

Distinct2281
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36315.52
Minimum108
Maximum1667000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:33:07.307725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile535
Q13280
median10200
Q327825
95-th percentile143000
Maximum1667000
Range1666892
Interquartile range (IQR)24545

Descriptive statistics

Standard deviation102758.29
Coefficient of variation (CV)2.8295971
Kurtosis77.340586
Mean36315.52
Median Absolute Deviation (MAD)8690
Skewness7.7583291
Sum3.631552 × 108
Variance1.0559266 × 1010
MonotonicityNot monotonic
2023-12-11T08:33:07.914709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4290 103
 
1.0%
5110 74
 
0.7%
6270 65
 
0.7%
5610 61
 
0.6%
10200 61
 
0.6%
13000 60
 
0.6%
10500 57
 
0.6%
4450 56
 
0.6%
6760 50
 
0.5%
5940 45
 
0.4%
Other values (2271) 9368
93.7%
ValueCountFrequency (%)
108 1
 
< 0.1%
110 4
< 0.1%
113 7
0.1%
117 1
 
< 0.1%
118 8
0.1%
127 8
0.1%
143 1
 
< 0.1%
153 2
 
< 0.1%
160 1
 
< 0.1%
163 1
 
< 0.1%
ValueCountFrequency (%)
1667000 1
< 0.1%
1542000 1
< 0.1%
1534000 1
< 0.1%
1489000 2
< 0.1%
1435000 1
< 0.1%
1321000 1
< 0.1%
1317000 1
< 0.1%
1280000 1
< 0.1%
1268000 1
< 0.1%
1262000 1
< 0.1%

Interactions

2023-12-11T08:33:05.325756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:04.715493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:05.032941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:05.419041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:04.837847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:05.133374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:05.535598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:04.938983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:33:05.229172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:33:08.012379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지목면 적전년지가결정지가
지목1.0000.0000.3890.385
면 적0.0001.0000.0000.000
전년지가0.3890.0001.0000.996
결정지가0.3850.0000.9961.000
2023-12-11T08:33:08.089375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면 적전년지가결정지가지목
면 적1.000-0.141-0.1490.000
전년지가-0.1411.0000.9830.153
결정지가-0.1490.9831.0000.151
지목0.0000.1530.1511.000

Missing values

2023-12-11T08:33:05.645566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:33:05.725798image/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

소 재 지지목면 적전년지가결정지가
36359하동군 화개면 정금리 산 89임13042.0719840
54520하동군 악양면 신성리 632-1대294.05460057800
58764하동군 악양면 중대리 436-4답31.01980022100
41318하동군 화개면 범왕리 45임159.0566605
72929하동군 악양면 입석리 825-1구74.028003100
22404하동군 하동읍 두곡리 893-5답36.01560016900
82699하동군 적량면 우계리 1279-2구481.027303160
50602하동군 악양면 축지리 481-3도30.049505610
42907하동군 화개면 범왕리 1183-16대740.05570063200
65996하동군 악양면 매계리 651-1도472.064307090
소 재 지지목면 적전년지가결정지가
12657하동군 하동읍 목도리 580-5제5.046205110
54123하동군 악양면 신성리 359-1도23.052805940
35071하동군 화개면 정금리 797답17.01650019100
93895하동군 적량면 동산리 1463-1답108.02950031300
7527하동군 하동읍 비파리 585전2346.01400015000
95220하동군 적량면 고절리 39답13.012601400
49594하동군 악양면 미점리 산 71-4임151.0489519
1137하동군 하동읍 읍내리 273-63전145.054405770
74392하동군 악양면 봉대리 453-13도8.01880020200
76387하동군 악양면 평사리 467대836.08340088100