Overview

Dataset statistics

Number of variables19
Number of observations83
Missing cells104
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 KiB
Average record size in memory156.5 B

Variable types

Text5
Categorical10
Numeric3
DateTime1

Dataset

Description국립공원내 도유재산(토지) 현황(재산분류,재산의소재,면적, 위치, 특수지, 본번, 부번, 공시지가, 재산가격, 총 사용허가면적 등) 제공
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055535/fileData.do

Alerts

재산분류-중 has constant value ""Constant
재산분류-소 has constant value ""Constant
공시지가 is highly overall correlated with 재산의소재-본번 and 4 other fieldsHigh correlation
용도지구 is highly overall correlated with 재산의소재-본번 and 4 other fieldsHigh correlation
재산의소재-위치 is highly overall correlated with 재산의소재-본번 and 5 other fieldsHigh correlation
비고(실제용도) is highly overall correlated with 재산의소재-위치 and 4 other fieldsHigh correlation
재산의소재-특수지 is highly overall correlated with 재산의소재-본번 and 9 other fieldsHigh correlation
지목-현황 is highly overall correlated with 재산의소재-본번 and 4 other fieldsHigh correlation
지목-공부 is highly overall correlated with 재산의소재-특수지 and 2 other fieldsHigh correlation
사용허가면적 2 is highly overall correlated with 재산의소재-특수지High correlation
재산의소재-본번 is highly overall correlated with 재산의소재-위치 and 4 other fieldsHigh correlation
재산의소재-부번 is highly overall correlated with 재산의소재-특수지High correlation
면적 is highly overall correlated with 재산의소재-특수지 and 1 other fieldsHigh correlation
사용허가면적 2 is highly imbalanced (69.4%)Imbalance
용도지구 is highly imbalanced (90.6%)Imbalance
재산의소재-부번 has 14 (16.9%) missing valuesMissing
총 사용허가면적 has 9 (10.8%) missing valuesMissing
비고 has 81 (97.6%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:54:18.359916
Analysis finished2024-03-15 01:54:24.696192
Duration6.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct83
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size792.0 B
2024-03-15T10:54:25.710679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6746988
Min length4

Characters and Unicode

Total characters388
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)100.0%

Sample

1st row지리산3
2nd row지리산4
3rd row지리산5
4th row지리산1
5th row지리산2
ValueCountFrequency (%)
지리산3 1
 
1.2%
내장산24 1
 
1.2%
덕유산4 1
 
1.2%
덕유산3 1
 
1.2%
덕유산2 1
 
1.2%
덕유산1 1
 
1.2%
내장산38 1
 
1.2%
내장산37 1
 
1.2%
내장산36 1
 
1.2%
내장산35 1
 
1.2%
Other values (73) 73
88.0%
2024-03-15T10:54:27.216547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 83
21.4%
1 40
10.3%
내 38
9.8%
장 38
9.8%
덕 25
 
6.4%
2 25
 
6.4%
유 25
 
6.4%
지 19
 
4.9%
리 19
 
4.9%
3 18
 
4.6%
Other values (10) 58
14.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 250
64.4%
Decimal Number 138
35.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 83
33.2%
내 38
15.2%
장 38
15.2%
덕 25
 
10.0%
유 25
 
10.0%
지 19
 
7.6%
리 19
 
7.6%
변 1
 
0.4%
반 1
 
0.4%
도 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 40
29.0%
2 25
18.1%
3 18
13.0%
5 9
 
6.5%
4 9
 
6.5%
6 8
 
5.8%
8 8
 
5.8%
7 8
 
5.8%
9 7
 
5.1%
0 6
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 250
64.4%
Common 138
35.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 83
33.2%
내 38
15.2%
장 38
15.2%
덕 25
 
10.0%
유 25
 
10.0%
지 19
 
7.6%
리 19
 
7.6%
변 1
 
0.4%
반 1
 
0.4%
도 1
 
0.4%
Common
ValueCountFrequency (%)
1 40
29.0%
2 25
18.1%
3 18
13.0%
5 9
 
6.5%
4 9
 
6.5%
6 8
 
5.8%
8 8
 
5.8%
7 8
 
5.8%
9 7
 
5.1%
0 6
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
64.4%
ASCII 138
35.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 83
33.2%
내 38
15.2%
장 38
15.2%
덕 25
 
10.0%
유 25
 
10.0%
지 19
 
7.6%
리 19
 
7.6%
변 1
 
0.4%
반 1
 
0.4%
도 1
 
0.4%
ASCII
ValueCountFrequency (%)
1 40
29.0%
2 25
18.1%
3 18
13.0%
5 9
 
6.5%
4 9
 
6.5%
6 8
 
5.8%
8 8
 
5.8%
7 8
 
5.8%
9 7
 
5.1%
0 6
 
4.3%

재산분류-중
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size792.0 B
행정재산
83 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
행정재산 83
100.0%

Length

2024-03-15T10:54:27.626666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:54:28.038259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
행정재산 83
100.0%

재산분류-소
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size792.0 B
공공용
83 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공용 83
100.0%

Length

2024-03-15T10:54:28.387647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:54:28.719850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공용 83
100.0%

재산의소재-위치
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size792.0 B
정읍시 내장동
38 
무주군 설천면 삼공리
25 
남원시 산내면 부운리
14 
남원시 산내면 덕동리
5 
부안군 변산면 격포리
 
1

Length

Max length11
Median length11
Mean length9.1686747
Min length7

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row남원시 산내면 덕동리
2nd row남원시 산내면 덕동리
3rd row남원시 산내면 덕동리
4th row남원시 산내면 덕동리
5th row남원시 산내면 덕동리

Common Values

ValueCountFrequency (%)
정읍시 내장동 38
45.8%
무주군 설천면 삼공리 25
30.1%
남원시 산내면 부운리 14
 
16.9%
남원시 산내면 덕동리 5
 
6.0%
부안군 변산면 격포리 1
 
1.2%

Length

2024-03-15T10:54:29.150806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:54:29.522609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정읍시 38
18.0%
내장동 38
18.0%
무주군 25
11.8%
설천면 25
11.8%
삼공리 25
11.8%
남원시 19
9.0%
산내면 19
9.0%
부운리 14
 
6.6%
덕동리 5
 
2.4%
부안군 1
 
0.5%
Other values (2) 2
 
0.9%

재산의소재-특수지
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size792.0 B
<NA>
66 
산
17 

Length

Max length4
Median length4
Mean length3.3855422
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row산
5th row산

Common Values

ValueCountFrequency (%)
<NA> 66
79.5%
산 17
 
20.5%

Length

2024-03-15T10:54:29.971548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:54:30.367123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 66
79.5%
산 17
 
20.5%

재산의소재-본번
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean392.73494
Minimum7
Maximum882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size875.0 B
2024-03-15T10:54:30.771766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13.1
Q1100.5
median257
Q3604
95-th percentile880
Maximum882
Range875
Interquartile range (IQR)503.5

Descriptive statistics

Standard deviation309.26364
Coefficient of variation (CV)0.7874615
Kurtosis-1.2653258
Mean392.73494
Median Absolute Deviation (MAD)178
Skewness0.48275109
Sum32597
Variance95644.002
MonotonicityNot monotonic
2024-03-15T10:54:31.230789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
879 8
 
9.6%
603 5
 
6.0%
202 5
 
6.0%
880 4
 
4.8%
882 3
 
3.6%
594 3
 
3.6%
78 3
 
3.6%
411 3
 
3.6%
92 2
 
2.4%
93 2
 
2.4%
Other values (41) 45
54.2%
ValueCountFrequency (%)
7 1
 
1.2%
8 2
2.4%
12 1
 
1.2%
13 1
 
1.2%
14 1
 
1.2%
78 3
3.6%
79 1
 
1.2%
80 1
 
1.2%
82 2
2.4%
88 1
 
1.2%
ValueCountFrequency (%)
882 3
 
3.6%
881 1
 
1.2%
880 4
4.8%
879 8
9.6%
874 1
 
1.2%
689 1
 
1.2%
606 2
 
2.4%
605 1
 
1.2%
603 5
6.0%
598 1
 
1.2%

재산의소재-부번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)21.7%
Missing14
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean5.3913043
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size875.0 B
2024-03-15T10:54:31.687509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile12.8
Maximum57
Range56
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.6134364
Coefficient of variation (CV)1.7831374
Kurtosis22.751462
Mean5.3913043
Median Absolute Deviation (MAD)2
Skewness4.6269631
Sum372
Variance92.418159
MonotonicityNot monotonic
2024-03-15T10:54:32.058666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 19
22.9%
1 15
18.1%
3 9
10.8%
4 6
 
7.2%
8 4
 
4.8%
7 4
 
4.8%
6 3
 
3.6%
5 2
 
2.4%
56 1
 
1.2%
57 1
 
1.2%
Other values (5) 5
 
6.0%
(Missing) 14
16.9%
ValueCountFrequency (%)
1 15
18.1%
2 19
22.9%
3 9
10.8%
4 6
 
7.2%
5 2
 
2.4%
6 3
 
3.6%
7 4
 
4.8%
8 4
 
4.8%
9 1
 
1.2%
10 1
 
1.2%
ValueCountFrequency (%)
57 1
 
1.2%
56 1
 
1.2%
23 1
 
1.2%
14 1
 
1.2%
11 1
 
1.2%
10 1
 
1.2%
9 1
 
1.2%
8 4
4.8%
7 4
4.8%
6 3
3.6%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2305.7229
Minimum10
Maximum43889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size875.0 B
2024-03-15T10:54:32.506536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile23
Q1134.5
median539
Q31607.5
95-th percentile15609.4
Maximum43889
Range43879
Interquartile range (IQR)1473

Descriptive statistics

Standard deviation6156.644
Coefficient of variation (CV)2.6701578
Kurtosis27.258352
Mean2305.7229
Median Absolute Deviation (MAD)470
Skewness4.8619496
Sum191375
Variance37904265
MonotonicityNot monotonic
2024-03-15T10:54:33.068445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 2
 
2.4%
522 2
 
2.4%
23 2
 
2.4%
202 2
 
2.4%
102 1
 
1.2%
2981 1
 
1.2%
569 1
 
1.2%
69 1
 
1.2%
1455 1
 
1.2%
1697 1
 
1.2%
Other values (69) 69
83.1%
ValueCountFrequency (%)
10 1
1.2%
13 2
2.4%
22 1
1.2%
23 2
2.4%
26 1
1.2%
33 1
1.2%
46 1
1.2%
60 1
1.2%
63 1
1.2%
66 1
1.2%
ValueCountFrequency (%)
43889 1
1.2%
21002 1
1.2%
20874 1
1.2%
17240 1
1.2%
16759 1
1.2%
5263 1
1.2%
5239 1
1.2%
4664 1
1.2%
3074 1
1.2%
2981 1
1.2%

지목-공부
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size792.0 B
임야
34 
답
16 
도로
9 
잡종지
8 
전
6 
Other values (5)
10 

Length

Max length4
Median length2
Mean length1.8915663
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row전
2nd row전
3rd row답
4th row임야
5th row임야

Common Values

ValueCountFrequency (%)
임야 34
41.0%
답 16
19.3%
도로 9
 
10.8%
잡종지 8
 
9.6%
전 6
 
7.2%
하천 3
 
3.6%
구거 2
 
2.4%
대지 2
 
2.4%
수도용지 2
 
2.4%
주차장 1
 
1.2%

Length

2024-03-15T10:54:33.308654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:54:33.584036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임야 34
41.0%
답 16
19.3%
도로 9
 
10.8%
잡종지 8
 
9.6%
전 6
 
7.2%
하천 3
 
3.6%
구거 2
 
2.4%
대지 2
 
2.4%
수도용지 2
 
2.4%
주차장 1
 
1.2%

지목-현황
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size792.0 B
녹지
21 
도로
12 
임야
12 
야영장
7 
잡종지
6 
Other values (15)
25 

Length

Max length6
Median length2
Mean length2.5662651
Min length2

Unique

Unique9 ?
Unique (%)10.8%

Sample

1st row주차장 궁터
2nd row궁터
3rd row주차장
4th row임야
5th row주차장

Common Values

ValueCountFrequency (%)
녹지 21
25.3%
도로 12
14.5%
임야 12
14.5%
야영장 7
 
8.4%
잡종지 6
 
7.2%
대지 5
 
6.0%
주차장 3
 
3.6%
차고지 2
 
2.4%
나대지 2
 
2.4%
구정수장 2
 
2.4%
Other values (10) 11
13.3%

Length

2024-03-15T10:54:34.057825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
녹지 23
26.4%
도로 12
13.8%
임야 12
13.8%
야영장 7
 
8.0%
잡종지 6
 
6.9%
대지 5
 
5.7%
주차장 4
 
4.6%
나대지 4
 
4.6%
궁터 2
 
2.3%
구정수장 2
 
2.3%
Other values (9) 10
11.5%

공시지가
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Memory size792.0 B
36 
586
 
3
11000
 
3
16000
 
2
649
 
2
Other values (30)
37 

Length

Max length6
Median length5
Mean length3.1566265
Min length2

Unique

Unique23 ?
Unique (%)27.7%

Sample

1st row2450
2nd row2450
3rd row3360
4th row305
5th row3360

Common Values

ValueCountFrequency (%)
36
43.4%
586 3
 
3.6%
11000 3
 
3.6%
16000 2
 
2.4%
649 2
 
2.4%
427 2
 
2.4%
21400 2
 
2.4%
3360 2
 
2.4%
11900 2
 
2.4%
805 2
 
2.4%
Other values (25) 27
32.5%

Length

2024-03-15T10:54:34.516937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
586 3
 
6.4%
11000 3
 
6.4%
16000 2
 
4.3%
649 2
 
4.3%
427 2
 
4.3%
21400 2
 
4.3%
3360 2
 
4.3%
11900 2
 
4.3%
805 2
 
4.3%
17600 2
 
4.3%
Other values (24) 25
53.2%

재산가격
Text

Distinct82
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size792.0 B
2024-03-15T10:54:35.624526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.4578313
Min length2

Characters and Unicode

Total characters536
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)97.6%

Sample

1st row30000000
2nd row70000000
3rd row133952
4th row324660
5th row104780
ValueCountFrequency (%)
70000000 2
 
2.4%
45540 1
 
1.2%
45549 1
 
1.2%
3450000 1
 
1.2%
49887 1
 
1.2%
90000000 1
 
1.2%
5091000 1
 
1.2%
169916 1
 
1.2%
759272 1
 
1.2%
1275868 1
 
1.2%
Other values (71) 71
86.6%
2024-03-15T10:54:37.099196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 189
35.3%
1 50
 
9.3%
3 45
 
8.4%
2 41
 
7.6%
4 40
 
7.5%
9 39
 
7.3%
8 36
 
6.7%
6 33
 
6.2%
5 31
 
5.8%
7 30
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 534
99.6%
Space Separator 2
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 189
35.4%
1 50
 
9.4%
3 45
 
8.4%
2 41
 
7.7%
4 40
 
7.5%
9 39
 
7.3%
8 36
 
6.7%
6 33
 
6.2%
5 31
 
5.8%
7 30
 
5.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 536
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 189
35.3%
1 50
 
9.3%
3 45
 
8.4%
2 41
 
7.6%
4 40
 
7.5%
9 39
 
7.3%
8 36
 
6.7%
6 33
 
6.2%
5 31
 
5.8%
7 30
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 189
35.3%
1 50
 
9.3%
3 45
 
8.4%
2 41
 
7.6%
4 40
 
7.5%
9 39
 
7.3%
8 36
 
6.7%
6 33
 
6.2%
5 31
 
5.8%
7 30
 
5.6%

총 사용허가면적
Text

MISSING 

Distinct70
Distinct (%)94.6%
Missing9
Missing (%)10.8%
Memory size792.0 B
2024-03-15T10:54:38.121352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1351351
Min length2

Characters and Unicode

Total characters232
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)89.2%

Sample

1st row16759
2nd row804
3rd row736
4th row4664
5th row5239
ValueCountFrequency (%)
202 2
 
2.7%
13 2
 
2.7%
23 2
 
2.7%
522 2
 
2.7%
2981 1
 
1.4%
1488 1
 
1.4%
3074 1
 
1.4%
1605 1
 
1.4%
1686 1
 
1.4%
148 1
 
1.4%
Other values (59) 59
80.8%
2024-03-15T10:54:39.543325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 34
14.7%
2 30
12.9%
6 29
12.5%
3 23
9.9%
9 22
9.5%
7 20
8.6%
5 19
8.2%
4 19
8.2%
0 17
7.3%
8 17
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 230
99.1%
Space Separator 2
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34
14.8%
2 30
13.0%
6 29
12.6%
3 23
10.0%
9 22
9.6%
7 20
8.7%
5 19
8.3%
4 19
8.3%
0 17
7.4%
8 17
7.4%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 34
14.7%
2 30
12.9%
6 29
12.5%
3 23
9.9%
9 22
9.5%
7 20
8.6%
5 19
8.2%
4 19
8.2%
0 17
7.3%
8 17
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 34
14.7%
2 30
12.9%
6 29
12.5%
3 23
9.9%
9 22
9.5%
7 20
8.6%
5 19
8.2%
4 19
8.2%
0 17
7.3%
8 17
7.3%

사용허가면적 1
Text

Distinct62
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Memory size792.0 B
2024-03-15T10:54:40.456797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.8072289
Min length2

Characters and Unicode

Total characters233
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)68.7%

Sample

1st row6582
2nd row804
3rd row736
4th row4664
5th row
ValueCountFrequency (%)
23 2
 
3.1%
522 2
 
3.1%
202 2
 
3.1%
13 2
 
3.1%
397 1
 
1.5%
3074 1
 
1.5%
69 1
 
1.5%
1455 1
 
1.5%
1697 1
 
1.5%
1605 1
 
1.5%
Other values (51) 51
78.5%
2024-03-15T10:54:41.851999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
15.5%
1 28
12.0%
6 27
11.6%
2 26
11.2%
9 20
8.6%
3 19
8.2%
4 17
7.3%
7 16
6.9%
5 15
6.4%
8 15
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 197
84.5%
Space Separator 36
 
15.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28
14.2%
6 27
13.7%
2 26
13.2%
9 20
10.2%
3 19
9.6%
4 17
8.6%
7 16
8.1%
5 15
7.6%
8 15
7.6%
0 14
7.1%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
36
15.5%
1 28
12.0%
6 27
11.6%
2 26
11.2%
9 20
8.6%
3 19
8.2%
4 17
7.3%
7 16
6.9%
5 15
6.4%
8 15
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
15.5%
1 28
12.0%
6 27
11.6%
2 26
11.2%
9 20
8.6%
3 19
8.2%
4 17
7.3%
7 16
6.9%
5 15
6.4%
8 15
6.4%

사용허가면적 2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size792.0 B
71 
<NA>
 
3
10177
 
1
5239
 
1
2013
 
1
Other values (6)
 
6

Length

Max length5
Median length2
Mean length2.3012048
Min length2

Unique

Unique9 ?
Unique (%)10.8%

Sample

1st row10177
2nd row
3rd row
4th row
5th row5239

Common Values

ValueCountFrequency (%)
71
85.5%
<NA> 3
 
3.6%
10177 1
 
1.2%
5239 1
 
1.2%
2013 1
 
1.2%
1537 1
 
1.2%
1706 1
 
1.2%
836 1
 
1.2%
1825 1
 
1.2%
1524 1
 
1.2%

Length

2024-03-15T10:54:42.296098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3
25.0%
10177 1
 
8.3%
5239 1
 
8.3%
2013 1
 
8.3%
1537 1
 
8.3%
1706 1
 
8.3%
836 1
 
8.3%
1825 1
 
8.3%
1524 1
 
8.3%
20874 1
 
8.3%

용도지구
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size792.0 B
자연환경
82 
 
1

Length

Max length4
Median length4
Mean length3.9759036
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
자연환경 82
98.8%
1
 
1.2%

Length

2024-03-15T10:54:42.765786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:54:42.983892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자연환경 82
100.0%

취득일자
Date

Distinct37
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size792.0 B
Minimum1948-10-02 00:00:00
Maximum1996-01-17 00:00:00
2024-03-15T10:54:43.167436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:43.486199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

비고(실제용도)
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Memory size792.0 B
임야
21 
녹지
11 
도로 녹지
6 
야영장
6 
도로
5 
Other values (27)
34 

Length

Max length19
Median length2
Mean length3.746988
Min length1

Unique

Unique23 ?
Unique (%)27.7%

Sample

1st row달궁터 주차장
2nd row달궁터
3rd row덕동야영장 급수탱크
4th row달궁야영장 급수탱크
5th row달궁야영장 주차장

Common Values

ValueCountFrequency (%)
임야 21
25.3%
녹지 11
13.3%
도로 녹지 6
 
7.2%
야영장 6
 
7.2%
도로 5
 
6.0%
잡종지 5
 
6.0%
구정수장 2
 
2.4%
대지 2
 
2.4%
탐방로 2
 
2.4%
묘포장 1
 
1.2%
Other values (22) 22
26.5%

Length

2024-03-15T10:54:43.757404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
임야 24
22.6%
녹지 19
17.9%
도로 13
12.3%
야영장 6
 
5.7%
잡종지 5
 
4.7%
주차장 5
 
4.7%
천 3
 
2.8%
나대지 3
 
2.8%
대지 3
 
2.8%
달궁야영장 2
 
1.9%
Other values (18) 23
21.7%

비고
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing81
Missing (%)97.6%
Memory size792.0 B
2024-03-15T10:54:44.315019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length20.5
Mean length20.5
Min length12

Characters and Unicode

Total characters41
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row12년중 사용허가 신청
2nd row12.4.13무주군에분할매각(83㎡) 당초 5346㎡
ValueCountFrequency (%)
12년중 1
16.7%
사용허가 1
16.7%
신청 1
16.7%
12.4.13무주군에분할매각(83㎡ 1
16.7%
당초 1
16.7%
5346㎡ 1
16.7%
2024-03-15T10:54:45.194679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
 
9.8%
1 3
 
7.3%
3 3
 
7.3%
2 2
 
4.9%
4 2
 
4.9%
㎡ 2
 
4.9%
. 2
 
4.9%
각 1
 
2.4%
매 1
 
2.4%
8 1
 
2.4%
Other values (20) 20
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
43.9%
Decimal Number 13
31.7%
Space Separator 4
 
9.8%
Other Symbol 2
 
4.9%
Other Punctuation 2
 
4.9%
Open Punctuation 1
 
2.4%
Close Punctuation 1
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
각 1
 
5.6%
매 1
 
5.6%
분 1
 
5.6%
당 1
 
5.6%
초 1
 
5.6%
할 1
 
5.6%
주 1
 
5.6%
에 1
 
5.6%
군 1
 
5.6%
무 1
 
5.6%
Other values (8) 8
44.4%
Decimal Number
ValueCountFrequency (%)
1 3
23.1%
3 3
23.1%
2 2
15.4%
4 2
15.4%
8 1
 
7.7%
5 1
 
7.7%
6 1
 
7.7%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23
56.1%
Hangul 18
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
각 1
 
5.6%
매 1
 
5.6%
분 1
 
5.6%
당 1
 
5.6%
초 1
 
5.6%
할 1
 
5.6%
주 1
 
5.6%
에 1
 
5.6%
군 1
 
5.6%
무 1
 
5.6%
Other values (8) 8
44.4%
Common
ValueCountFrequency (%)
4
17.4%
1 3
13.0%
3 3
13.0%
2 2
8.7%
4 2
8.7%
㎡ 2
8.7%
. 2
8.7%
8 1
 
4.3%
( 1
 
4.3%
) 1
 
4.3%
Other values (2) 2
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
51.2%
Hangul 18
43.9%
CJK Compat 2
 
4.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4
19.0%
1 3
14.3%
3 3
14.3%
2 2
9.5%
4 2
9.5%
. 2
9.5%
8 1
 
4.8%
( 1
 
4.8%
) 1
 
4.8%
5 1
 
4.8%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%
Hangul
ValueCountFrequency (%)
각 1
 
5.6%
매 1
 
5.6%
분 1
 
5.6%
당 1
 
5.6%
초 1
 
5.6%
할 1
 
5.6%
주 1
 
5.6%
에 1
 
5.6%
군 1
 
5.6%
무 1
 
5.6%
Other values (8) 8
44.4%

Interactions

2024-03-15T10:54:22.651736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:21.531132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:21.995072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:22.873235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:21.659439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:22.260923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:23.110901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:21.798521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:54:22.517343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:54:45.590479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분재산의소재-위치재산의소재-본번재산의소재-부번면적지목-공부지목-현황공시지가재산가격총 사용허가면적사용허가면적 1사용허가면적 2용도지구취득일자비고(실제용도)비고
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
재산의소재-위치1.0001.0000.8500.0000.6150.4720.8860.9910.0000.9860.0000.3821.0001.0000.9250.000
재산의소재-본번1.0000.8501.0000.0000.4010.7320.8910.9150.0000.9560.8550.2800.6560.9950.8570.000
재산의소재-부번1.0000.0000.0001.0000.0000.2490.0000.0001.0000.0000.8920.0000.0000.8430.0000.000
면적1.0000.6150.4010.0001.0000.6540.4650.9771.0001.0000.0000.8330.0000.0000.7250.000
지목-공부1.0000.4720.7320.2490.6541.0000.9240.8690.9450.5450.0000.6430.0000.8870.9240.000
지목-현황1.0000.8860.8910.0000.4650.9241.0000.9280.0000.9630.8200.5980.0000.9600.9870.000
공시지가1.0000.9910.9150.0000.9770.8690.9281.0000.9810.9970.0000.8421.0000.9480.8940.000
재산가격1.0000.0000.0001.0001.0000.9450.0000.9811.0000.9940.9970.0001.0000.9940.9380.000
총 사용허가면적1.0000.9860.9560.0001.0000.5450.9630.9970.9941.0001.0001.0001.0000.9570.9520.000
사용허가면적 11.0000.0000.8550.8920.0000.0000.8200.0000.9971.0001.0000.0000.0000.9620.9550.000
사용허가면적 21.0000.3820.2800.0000.8330.6430.5980.8420.0001.0000.0001.0000.0000.0000.745NaN
용도지구1.0001.0000.6560.0000.0000.0000.0001.0001.0001.0000.0000.0001.0001.000NaNNaN
취득일자1.0001.0000.9950.8430.0000.8870.9600.9480.9940.9570.9620.0001.0001.0000.9320.000
비고(실제용도)1.0000.9250.8570.0000.7250.9240.9870.8940.9380.9520.9550.745NaN0.9321.0000.000
비고0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaNNaN0.0000.0001.000
2024-03-15T10:54:46.001635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공시지가용도지구재산의소재-위치비고(실제용도)재산의소재-특수지지목-현황지목-공부사용허가면적 2
공시지가1.0000.7700.6900.3841.0000.4640.4190.392
용도지구0.7701.0000.9811.0001.0000.0000.0000.000
재산의소재-위치0.6900.9811.0000.6141.0000.5370.2040.158
비고(실제용도)0.3841.0000.6141.0001.0000.7590.5500.311
재산의소재-특수지1.0001.0001.0001.0001.0001.0001.0001.000
지목-현황0.4640.0000.5370.7591.0001.0000.5400.206
지목-공부0.4190.0000.2040.5501.0000.5401.0000.243
사용허가면적 20.3920.0000.1580.3111.0000.2060.2431.000
2024-03-15T10:54:46.322906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재산의소재-본번재산의소재-부번면적재산의소재-위치재산의소재-특수지지목-공부지목-현황공시지가사용허가면적 2용도지구비고(실제용도)
재산의소재-본번1.0000.128-0.2890.6841.0000.4420.5790.5160.1240.6330.444
재산의소재-부번0.1281.000-0.2680.0001.0000.1460.0000.0000.0000.0000.000
면적-0.289-0.2681.0000.2691.0000.3160.1890.6430.4760.0000.356
재산의소재-위치0.6840.0000.2691.0001.0000.2040.5370.6900.1580.9810.614
재산의소재-특수지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지목-공부0.4420.1460.3160.2041.0001.0000.5400.4190.2430.0000.550
지목-현황0.5790.0000.1890.5371.0000.5401.0000.4640.2060.0000.759
공시지가0.5160.0000.6430.6901.0000.4190.4641.0000.3920.7700.384
사용허가면적 20.1240.0000.4760.1581.0000.2430.2060.3921.0000.0000.311
용도지구0.6330.0000.0000.9811.0000.0000.0000.7700.0001.0001.000
비고(실제용도)0.4440.0000.3560.6141.0000.5500.7590.3840.3111.0001.000

Missing values

2024-03-15T10:54:23.375924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:54:24.012705image/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.
2024-03-15T10:54:24.478965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분재산분류-중재산분류-소재산의소재-위치재산의소재-특수지재산의소재-본번재산의소재-부번면적지목-공부지목-현황공시지가재산가격총 사용허가면적사용허가면적 1사용허가면적 2용도지구취득일자비고(실제용도)비고
0지리산3행정재산공공용남원시 산내면 덕동리<NA>287<NA>16759전주차장 궁터24503000000016759658210177자연환경1987-09-01달궁터 주차장<NA>
1지리산4행정재산공공용남원시 산내면 덕동리<NA>300<NA>804전궁터245070000000804804자연환경1987-09-01달궁터<NA>
2지리산5행정재산공공용남원시 산내면 덕동리<NA>822736답주차장3360133952736736자연환경1986-04-15덕동야영장 급수탱크12년중 사용허가 신청
3지리산1행정재산공공용남원시 산내면 덕동리산10424664임야임야30532466046644664자연환경1987-12-29달궁야영장 급수탱크<NA>
4지리산2행정재산공공용남원시 산내면 덕동리산10725239임야주차장336010478052395239자연환경1986-10-03달궁야영장 주차장<NA>
5지리산11행정재산공공용남원시 산내면 부운리<NA>214<NA>22전잡종지23701958<NA>자연환경1983-12-30임야<NA>
6지리산12행정재산공공용남원시 산내면 부운리<NA>216417240잡종지잡종지904002831316501724017240자연환경1983-12-30잡종지<NA>
7지리산13행정재산공공용남원시 산내면 부운리<NA>223<NA>137전잡종지5670012193137137자연환경1986-01-30임야<NA>
8지리산14행정재산공공용남원시 산내면 부운리<NA>225<NA>331임야잡종지2916951<NA>자연환경1983-12-30임야<NA>
9지리산15행정재산공공용남원시 산내면 부운리<NA>226<NA>2911전잡종지59050000000<NA>자연환경1983-12-30임야<NA>
구분재산분류-중재산분류-소재산의소재-위치재산의소재-특수지재산의소재-본번재산의소재-부번면적지목-공부지목-현황공시지가재산가격총 사용허가면적사용허가면적 1사용허가면적 2용도지구취득일자비고(실제용도)비고
73덕유산17행정재산공공용무주군 설천면 삼공리<NA>8802288하천녹지3139200288288자연환경1979-12-11녹지<NA>
74덕유산18행정재산공공용무주군 설천면 삼공리<NA>880333임야녹지3597003333자연환경1979-07-23녹지<NA>
75덕유산19행정재산공공용무주군 설천면 삼공리<NA>880413임야녹지1417001313자연환경1979-07-24녹지<NA>
76덕유산20행정재산공공용무주군 설천면 삼공리<NA>881<NA>982잡종지녹지703800982982자연환경1979-07-23잡종지<NA>
77덕유산21행정재산공공용무주군 설천면 삼공리<NA>88211527잡종지녹지64430015271527자연환경1979-07-23잡종지<NA>
78덕유산22행정재산공공용무주군 설천면 삼공리<NA>8822202임야녹지2201800202202자연환경1979-07-23잡종지<NA>
79덕유산23행정재산공공용무주군 설천면 삼공리<NA>8823155임야녹지1689500155155자연환경1979-07-23잡종지<NA>
80덕유산24행정재산공공용무주군 설천면 삼공리<NA>411<NA>20874주차장주차장13300039660600002087420874자연환경1977-12-30주차장<NA>
81덕유산25행정재산공공용무주군 설천면 삼공리<NA>41185263대지대지98000000052635263자연환경1977-12-30대지12.4.13무주군에분할매각(83㎡) 당초 5346㎡
82변산반도1행정재산공공용부안군 변산면 격포리<NA>5142562임야임야486027313201948-10-02<NA><NA>