Overview

Dataset statistics

Number of variables19
Number of observations89
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 KiB
Average record size in memory153.5 B

Variable types

Categorical14
Text5

Dataset

Description국립공원내도유재산토지현황
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202208

Alerts

재산분류.1 is highly overall correlated with 구분 and 8 other fieldsHigh correlation
재산의소재 is highly overall correlated with 재산분류 and 6 other fieldsHigh correlation
재산분류 is highly overall correlated with 구분 and 8 other fieldsHigh correlation
용도지구 is highly overall correlated with 구분 and 7 other fieldsHigh correlation
지목 is highly overall correlated with 재산분류 and 6 other fieldsHigh correlation
구분 is highly overall correlated with 재산분류 and 3 other fieldsHigh correlation
재산의소재.1 is highly overall correlated with 구분 and 7 other fieldsHigh correlation
재산의소재.3 is highly overall correlated with 재산분류 and 2 other fieldsHigh correlation
지목.1 is highly overall correlated with 재산분류 and 7 other fieldsHigh correlation
사용허가면적(㎡) .2 is highly overall correlated with 재산분류 and 3 other fieldsHigh correlation
취득일자 is highly overall correlated with 지목.1 and 1 other fieldsHigh correlation
비고(실제용도) is highly overall correlated with 지목 and 3 other fieldsHigh correlation
비고 is highly overall correlated with 비고(실제용도)High correlation
재산분류 is highly imbalanced (74.8%)Imbalance
재산분류.1 is highly imbalanced (74.8%)Imbalance
재산의소재.1 is highly imbalanced (50.2%)Imbalance
사용허가면적(㎡) .2 is highly imbalanced (65.6%)Imbalance
용도지구 is highly imbalanced (70.6%)Imbalance
비고 is highly imbalanced (88.8%)Imbalance

Reproduction

Analysis started2024-03-14 02:50:47.772142
Analysis finished2024-03-14 02:50:50.075899
Duration2.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
1
 
4
6
 
3
11
 
3
2
 
3
3
 
3
Other values (39)
73 

Length

Max length4
Median length2
Mean length1.7303371
Min length1

Unique

Unique19 ?
Unique (%)21.3%

Sample

1st row연번
2nd row계
3rd row지리산
4th row3
5th row4

Common Values

ValueCountFrequency (%)
1 4
 
4.5%
6 3
 
3.4%
11 3
 
3.4%
2 3
 
3.4%
3 3
 
3.4%
4 3
 
3.4%
12 3
 
3.4%
5 3
 
3.4%
13 3
 
3.4%
17 3
 
3.4%
Other values (34) 58
65.2%

Length

2024-03-14T11:50:50.131707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 4
 
4.5%
6 3
 
3.4%
14 3
 
3.4%
10 3
 
3.4%
9 3
 
3.4%
8 3
 
3.4%
7 3
 
3.4%
19 3
 
3.4%
18 3
 
3.4%
16 3
 
3.4%
Other values (34) 58
65.2%

재산분류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
행정재산
83 
-
 
5
중
 
1

Length

Max length4
Median length4
Mean length3.7977528
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row중
2nd row-
3rd row-
4th row행정재산
5th row행정재산

Common Values

ValueCountFrequency (%)
행정재산 83
93.3%
- 5
 
5.6%
중 1
 
1.1%

Length

2024-03-14T11:50:50.247357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:50:50.377170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
행정재산 83
93.3%
5
 
5.6%
중 1
 
1.1%

재산분류.1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
공공용
83 
-
 
5
소
 
1

Length

Max length3
Median length3
Mean length2.8651685
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row소
2nd row-
3rd row-
4th row공공용
5th row공공용

Common Values

ValueCountFrequency (%)
공공용 83
93.3%
- 5
 
5.6%
소 1
 
1.1%

Length

2024-03-14T11:50:50.467748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:50:50.586803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공용 83
93.3%
5
 
5.6%
소 1
 
1.1%

재산의소재
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
정읍시 내장동
38 
무주군 설천면 삼공리
25 
남원시 산내면 부운리
14 
남원시 산내면 덕동리
5 
위치
 
1
Other values (6)
6 

Length

Max length11
Median length11
Mean length8.6741573
Min length1

Unique

Unique7 ?
Unique (%)7.9%

Sample

1st row위치
2nd row83
3rd row19
4th row남원시 산내면 덕동리
5th row남원시 산내면 덕동리

Common Values

ValueCountFrequency (%)
정읍시 내장동 38
42.7%
무주군 설천면 삼공리 25
28.1%
남원시 산내면 부운리 14
 
15.7%
남원시 산내면 덕동리 5
 
5.6%
위치 1
 
1.1%
83 1
 
1.1%
19 1
 
1.1%
38 1
 
1.1%
25 1
 
1.1%
1 1
 
1.1%

Length

2024-03-14T11:50:50.705374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정읍시 38
17.5%
내장동 38
17.5%
무주군 25
11.5%
설천면 25
11.5%
삼공리 25
11.5%
남원시 19
8.8%
산내면 19
8.8%
부운리 14
 
6.5%
덕동리 5
 
2.3%
25 1
 
0.5%
Other values (8) 8
 
3.7%

재산의소재.1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
-
71 
산
17 
특수지
 
1

Length

Max length3
Median length1
Mean length1.0224719
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row특수지
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 71
79.8%
산 17
 
19.1%
특수지 1
 
1.1%

Length

2024-03-14T11:50:50.805006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:50:50.909952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
71
79.8%
산 17
 
19.1%
특수지 1
 
1.1%

재산의소재.2
Text

Distinct54
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-03-14T11:50:51.062150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.6179775
Min length1

Characters and Unicode

Total characters233
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)43.8%

Sample

1st row본번
2nd row필지
3rd row-
4th row287
5th row300
ValueCountFrequency (%)
879 8
 
9.0%
202 5
 
5.6%
603 5
 
5.6%
4
 
4.5%
880 4
 
4.5%
411 3
 
3.4%
882 3
 
3.4%
78 3
 
3.4%
594 3
 
3.4%
8 2
 
2.2%
Other values (44) 49
55.1%
2024-03-14T11:50:51.388406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 42
18.0%
2 36
15.5%
1 25
10.7%
0 23
9.9%
9 21
9.0%
7 20
8.6%
4 18
7.7%
6 15
 
6.4%
5 13
 
5.6%
3 12
 
5.2%
Other values (5) 8
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 225
96.6%
Dash Punctuation 4
 
1.7%
Other Letter 4
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 42
18.7%
2 36
16.0%
1 25
11.1%
0 23
10.2%
9 21
9.3%
7 20
8.9%
4 18
8.0%
6 15
 
6.7%
5 13
 
5.8%
3 12
 
5.3%
Other Letter
ValueCountFrequency (%)
필 1
25.0%
지 1
25.0%
본 1
25.0%
번 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 229
98.3%
Hangul 4
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
8 42
18.3%
2 36
15.7%
1 25
10.9%
0 23
10.0%
9 21
9.2%
7 20
8.7%
4 18
7.9%
6 15
 
6.6%
5 13
 
5.7%
3 12
 
5.2%
Hangul
ValueCountFrequency (%)
필 1
25.0%
지 1
25.0%
본 1
25.0%
번 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
98.3%
Hangul 4
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 42
18.3%
2 36
15.7%
1 25
10.9%
0 23
10.0%
9 21
9.2%
7 20
8.7%
4 18
7.9%
6 15
 
6.6%
5 13
 
5.7%
3 12
 
5.2%
Hangul
ValueCountFrequency (%)
필 1
25.0%
지 1
25.0%
본 1
25.0%
번 1
25.0%

재산의소재.3
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size844.0 B
2
19 
-
19 
1
15 
3
9 
4
6 
Other values (12)
21 

Length

Max length2
Median length1
Mean length1.0786517
Min length1

Unique

Unique8 ?
Unique (%)9.0%

Sample

1st row부번
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
2 19
21.3%
- 19
21.3%
1 15
16.9%
3 9
10.1%
4 6
 
6.7%
8 4
 
4.5%
7 4
 
4.5%
6 3
 
3.4%
5 2
 
2.2%
부번 1
 
1.1%
Other values (7) 7
 
7.9%

Length

2024-03-14T11:50:51.552504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 19
21.3%
19
21.3%
1 15
16.9%
3 9
10.1%
4 6
 
6.7%
8 4
 
4.5%
7 4
 
4.5%
6 3
 
3.4%
5 2
 
2.2%
부번 1
 
1.1%
Other values (7) 7
 
7.9%

면적(㎡)
Text

Distinct85
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-03-14T11:50:51.848188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.2696629
Min length1

Characters and Unicode

Total characters469
Distinct characters13
Distinct categories4 ?
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 (%)91.0%

Sample

1st row-
2nd row 165,238
3rd row 124,116
4th row 16,759
5th row 804
ValueCountFrequency (%)
23 2
 
2.2%
13 2
 
2.2%
562 2
 
2.2%
522 2
 
2.2%
202 2
 
2.2%
1,527 1
 
1.1%
5,263 1
 
1.1%
397 1
 
1.1%
1,774 1
 
1.1%
2,981 1
 
1.1%
Other values (74) 74
83.1%
2024-03-14T11:50:52.186832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
147
31.3%
1 45
 
9.6%
2 44
 
9.4%
6 34
 
7.2%
, 31
 
6.6%
3 29
 
6.2%
9 25
 
5.3%
5 24
 
5.1%
4 23
 
4.9%
8 23
 
4.9%
Other values (3) 44
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 290
61.8%
Space Separator 147
31.3%
Other Punctuation 31
 
6.6%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 45
15.5%
2 44
15.2%
6 34
11.7%
3 29
10.0%
9 25
8.6%
5 24
8.3%
4 23
7.9%
8 23
7.9%
0 22
7.6%
7 21
7.2%
Space Separator
ValueCountFrequency (%)
147
100.0%
Other Punctuation
ValueCountFrequency (%)
, 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147
31.3%
1 45
 
9.6%
2 44
 
9.4%
6 34
 
7.2%
, 31
 
6.6%
3 29
 
6.2%
9 25
 
5.3%
5 24
 
5.1%
4 23
 
4.9%
8 23
 
4.9%
Other values (3) 44
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147
31.3%
1 45
 
9.6%
2 44
 
9.4%
6 34
 
7.2%
, 31
 
6.6%
3 29
 
6.2%
9 25
 
5.3%
5 24
 
5.1%
4 23
 
4.9%
8 23
 
4.9%
Other values (3) 44
 
9.4%

지목
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size844.0 B
임야
34 
답
16 
도로
9 
잡종지
8 
전
6 
Other values (8)
16 

Length

Max length4
Median length2
Mean length1.8651685
Min length1

Unique

Unique3 ?
Unique (%)3.4%

Sample

1st row공부
2nd row-
3rd row-
4th row전
5th row전

Common Values

ValueCountFrequency (%)
임야 34
38.2%
답 16
18.0%
도로 9
 
10.1%
잡종지 8
 
9.0%
전 6
 
6.7%
- 4
 
4.5%
하천 3
 
3.4%
구거 2
 
2.2%
대지 2
 
2.2%
수도용지 2
 
2.2%
Other values (3) 3
 
3.4%

Length

2024-03-14T11:50:52.296232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
임야 34
38.2%
답 16
18.0%
도로 9
 
10.1%
잡종지 8
 
9.0%
전 6
 
6.7%
5
 
5.6%
하천 3
 
3.4%
구거 2
 
2.2%
대지 2
 
2.2%
수도용지 2
 
2.2%
Other values (2) 2
 
2.2%

지목.1
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size844.0 B
녹지
21 
임야
12 
도로
12 
야영장
7 
잡종지
6 
Other values (18)
31 

Length

Max length6
Median length2
Mean length2.494382
Min length1

Unique

Unique11 ?
Unique (%)12.4%

Sample

1st row현황
2nd row-
3rd row-
4th row주차장,궁터
5th row궁터

Common Values

ValueCountFrequency (%)
녹지 21
23.6%
임야 12
13.5%
도로 12
13.5%
야영장 7
 
7.9%
잡종지 6
 
6.7%
대지 5
 
5.6%
- 4
 
4.5%
주차장 3
 
3.4%
녹지,나대지 2
 
2.2%
나대지 2
 
2.2%
Other values (13) 15
16.9%

Length

2024-03-14T11:50:52.395393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
녹지 21
23.3%
도로 12
13.3%
임야 12
13.3%
야영장 7
 
7.8%
잡종지 6
 
6.7%
대지 5
 
5.6%
5
 
5.6%
주차장 3
 
3.3%
차고지 2
 
2.2%
구정수장 2
 
2.2%
Other values (13) 15
16.7%

공시지가(㎡/원)
Categorical

Distinct35
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Memory size844.0 B
-
42 
586
 
3
11,000
 
3
17,600
 
2
3,360
 
2
Other values (30)
37 

Length

Max length9
Median length8
Mean length4.8764045
Min length3

Unique

Unique23 ?
Unique (%)25.8%

Sample

1st row -
2nd row -
3rd row -
4th row 2,450
5th row 2,450

Common Values

ValueCountFrequency (%)
- 42
47.2%
586 3
 
3.4%
11,000 3
 
3.4%
17,600 2
 
2.2%
3,360 2
 
2.2%
649 2
 
2.2%
427 2
 
2.2%
2,450 2
 
2.2%
805 2
 
2.2%
16,000 2
 
2.2%
Other values (25) 27
30.3%

Length

2024-03-14T11:50:52.493031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
42
47.2%
11,000 3
 
3.4%
586 3
 
3.4%
2,450 2
 
2.2%
11,900 2
 
2.2%
16,000 2
 
2.2%
805 2
 
2.2%
21,400 2
 
2.2%
427 2
 
2.2%
649 2
 
2.2%
Other values (25) 27
30.3%

재산가격
Text

Distinct86
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-03-14T11:50:52.732344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.9775281
Min length3

Characters and Unicode

Total characters888
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)93.3%

Sample

1st row -
2nd row 911,443,210
3rd row 441,525,479
4th row 30,000,000
5th row 70,000,000
ValueCountFrequency (%)
2
 
2.2%
70,000,000 2
 
2.2%
2,731,320 2
 
2.2%
45,540 1
 
1.1%
30,000,000 1
 
1.1%
49,887 1
 
1.1%
90,000,000 1
 
1.1%
5,091,000 1
 
1.1%
122,833,736 1
 
1.1%
169,916 1
 
1.1%
Other values (76) 76
85.4%
2024-03-14T11:50:53.117637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 191
21.5%
178
20.0%
, 131
14.8%
1 56
 
6.3%
3 53
 
6.0%
2 48
 
5.4%
4 47
 
5.3%
9 41
 
4.6%
8 37
 
4.2%
5 35
 
3.9%
Other values (3) 71
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 577
65.0%
Space Separator 178
 
20.0%
Other Punctuation 131
 
14.8%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 191
33.1%
1 56
 
9.7%
3 53
 
9.2%
2 48
 
8.3%
4 47
 
8.1%
9 41
 
7.1%
8 37
 
6.4%
5 35
 
6.1%
6 35
 
6.1%
7 34
 
5.9%
Space Separator
ValueCountFrequency (%)
178
100.0%
Other Punctuation
ValueCountFrequency (%)
, 131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 888
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 191
21.5%
178
20.0%
, 131
14.8%
1 56
 
6.3%
3 53
 
6.0%
2 48
 
5.4%
4 47
 
5.3%
9 41
 
4.6%
8 37
 
4.2%
5 35
 
3.9%
Other values (3) 71
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 191
21.5%
178
20.0%
, 131
14.8%
1 56
 
6.3%
3 53
 
6.0%
2 48
 
5.4%
4 47
 
5.3%
9 41
 
4.6%
8 37
 
4.2%
5 35
 
3.9%
Other values (3) 71
 
8.0%

사용허가면적(㎡)
Text

Distinct76
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-03-14T11:50:53.315021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.1348315
Min length3

Characters and Unicode

Total characters457
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)78.7%

Sample

1st row 계
2nd row 91,755
3rd row 51,195
4th row 16,759
5th row 804
ValueCountFrequency (%)
미사용 9
 
10.1%
522 2
 
2.2%
23 2
 
2.2%
202 2
 
2.2%
2
 
2.2%
13 2
 
2.2%
148 1
 
1.1%
992 1
 
1.1%
1,488 1
 
1.1%
3,074 1
 
1.1%
Other values (66) 66
74.2%
2024-03-14T11:50:53.630248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
150
32.8%
1 38
 
8.3%
2 32
 
7.0%
6 29
 
6.3%
, 28
 
6.1%
3 24
 
5.3%
5 24
 
5.3%
9 24
 
5.3%
7 21
 
4.6%
4 20
 
4.4%
Other values (7) 67
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 249
54.5%
Space Separator 150
32.8%
Other Punctuation 28
 
6.1%
Other Letter 28
 
6.1%
Dash Punctuation 2
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38
15.3%
2 32
12.9%
6 29
11.6%
3 24
9.6%
5 24
9.6%
9 24
9.6%
7 21
8.4%
4 20
8.0%
8 19
7.6%
0 18
7.2%
Other Letter
ValueCountFrequency (%)
미 9
32.1%
사 9
32.1%
용 9
32.1%
계 1
 
3.6%
Space Separator
ValueCountFrequency (%)
150
100.0%
Other Punctuation
ValueCountFrequency (%)
, 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 429
93.9%
Hangul 28
 
6.1%

Most frequent character per script

Common
ValueCountFrequency (%)
150
35.0%
1 38
 
8.9%
2 32
 
7.5%
6 29
 
6.8%
, 28
 
6.5%
3 24
 
5.6%
5 24
 
5.6%
9 24
 
5.6%
7 21
 
4.9%
4 20
 
4.7%
Other values (3) 39
 
9.1%
Hangul
ValueCountFrequency (%)
미 9
32.1%
사 9
32.1%
용 9
32.1%
계 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 429
93.9%
Hangul 28
 
6.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
150
35.0%
1 38
 
8.9%
2 32
 
7.5%
6 29
 
6.8%
, 28
 
6.5%
3 24
 
5.6%
5 24
 
5.6%
9 24
 
5.6%
7 21
 
4.9%
4 20
 
4.7%
Other values (3) 39
 
9.1%
Hangul
ValueCountFrequency (%)
미 9
32.1%
사 9
32.1%
용 9
32.1%
계 1
 
3.6%

사용허가면적(㎡) .1
Text

Distinct67
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-03-14T11:50:53.866921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.8988764
Min length3

Characters and Unicode

Total characters436
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)69.7%

Sample

1st row 무상
2nd row 66,898
3rd row 33,766
4th row 6,582
5th row 804
ValueCountFrequency (%)
19
 
21.3%
202 2
 
2.2%
522 2
 
2.2%
23 2
 
2.2%
13 2
 
2.2%
63 1
 
1.1%
1,488 1
 
1.1%
3,074 1
 
1.1%
1,605 1
 
1.1%
1,686 1
 
1.1%
Other values (57) 57
64.0%
2024-03-14T11:50:54.370195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
178
40.8%
6 31
 
7.1%
1 29
 
6.7%
2 28
 
6.4%
9 22
 
5.0%
3 21
 
4.8%
, 21
 
4.8%
- 19
 
4.4%
8 19
 
4.4%
4 18
 
4.1%
Other values (5) 50
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 216
49.5%
Space Separator 178
40.8%
Other Punctuation 21
 
4.8%
Dash Punctuation 19
 
4.4%
Other Letter 2
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 31
14.4%
1 29
13.4%
2 28
13.0%
9 22
10.2%
3 21
9.7%
8 19
8.8%
4 18
8.3%
7 17
7.9%
5 16
7.4%
0 15
6.9%
Other Letter
ValueCountFrequency (%)
무 1
50.0%
상 1
50.0%
Space Separator
ValueCountFrequency (%)
178
100.0%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 434
99.5%
Hangul 2
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
178
41.0%
6 31
 
7.1%
1 29
 
6.7%
2 28
 
6.5%
9 22
 
5.1%
3 21
 
4.8%
, 21
 
4.8%
- 19
 
4.4%
8 19
 
4.4%
4 18
 
4.1%
Other values (3) 48
 
11.1%
Hangul
ValueCountFrequency (%)
무 1
50.0%
상 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 434
99.5%
Hangul 2
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
178
41.0%
6 31
 
7.1%
1 29
 
6.7%
2 28
 
6.5%
9 22
 
5.1%
3 21
 
4.8%
, 21
 
4.8%
- 19
 
4.4%
8 19
 
4.4%
4 18
 
4.1%
Other values (3) 48
 
11.1%
Hangul
ValueCountFrequency (%)
무 1
50.0%
상 1
50.0%

사용허가면적(㎡) .2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size844.0 B
-
73 
-
 
3
유상
 
1
24,857
 
1
17,429
 
1
Other values (10)
10 

Length

Max length8
Median length3
Mean length3.505618
Min length1

Unique

Unique13 ?
Unique (%)14.6%

Sample

1st row 유상
2nd row 24,857
3rd row 17,429
4th row 10,177
5th row -

Common Values

ValueCountFrequency (%)
- 73
82.0%
- 3
 
3.4%
유상 1
 
1.1%
24,857 1
 
1.1%
17,429 1
 
1.1%
10,177 1
 
1.1%
5,239 1
 
1.1%
2,013 1
 
1.1%
7,428 1
 
1.1%
1,537 1
 
1.1%
Other values (5) 5
 
5.6%

Length

2024-03-14T11:50:54.484293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
76
85.4%
유상 1
 
1.1%
24,857 1
 
1.1%
17,429 1
 
1.1%
10,177 1
 
1.1%
5,239 1
 
1.1%
2,013 1
 
1.1%
7,428 1
 
1.1%
1,537 1
 
1.1%
1,706 1
 
1.1%
Other values (4) 4
 
4.5%

용도지구
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
자연환경
82 
-
 
5
-
 
2

Length

Max length4
Median length4
Mean length3.8089888
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자연환경 82
92.1%
- 5
 
5.6%
- 2
 
2.2%

Length

2024-03-14T11:50:54.587431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:50:54.680923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자연환경 82
92.1%
7
 
7.9%

취득일자
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Memory size844.0 B
1979-07-23
14 
1983-12-30
10 
1972-10-10
 
5
1984-07-03
 
5
-
 
4
Other values (34)
51 

Length

Max length10
Median length10
Mean length9.4831461
Min length1

Unique

Unique23 ?
Unique (%)25.8%

Sample

1st row -
2nd row -
3rd row -
4th row1987-09-01
5th row1987-09-01

Common Values

ValueCountFrequency (%)
1979-07-23 14
 
15.7%
1983-12-30 10
 
11.2%
1972-10-10 5
 
5.6%
1984-07-03 5
 
5.6%
- 4
 
4.5%
1977-12-30 4
 
4.5%
1976-02-19 4
 
4.5%
1974-12-02 3
 
3.4%
1977-05-26 3
 
3.4%
1974-12-10 2
 
2.2%
Other values (29) 35
39.3%

Length

2024-03-14T11:50:54.799119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1979-07-23 14
15.7%
1983-12-30 10
 
11.2%
6
 
6.7%
1972-10-10 5
 
5.6%
1984-07-03 5
 
5.6%
1977-12-30 4
 
4.5%
1976-02-19 4
 
4.5%
1974-12-02 3
 
3.4%
1977-05-26 3
 
3.4%
1987-09-01 2
 
2.2%
Other values (28) 33
37.1%

비고(실제용도)
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Memory size844.0 B
임야
21 
녹지
11 
-
6 
도로, 녹지
6 
야영장
6 
Other values (28)
39 

Length

Max length19
Median length11
Mean length3.5617978
Min length1

Unique

Unique23 ?
Unique (%)25.8%

Sample

1st row-
2nd row-
3rd row-
4th row달궁터,주차장
5th row달궁터

Common Values

ValueCountFrequency (%)
임야 21
23.6%
녹지 11
12.4%
- 6
 
6.7%
도로, 녹지 6
 
6.7%
야영장 6
 
6.7%
잡종지 5
 
5.6%
도로 5
 
5.6%
구정수장 2
 
2.2%
탐방로 2
 
2.2%
대지 2
 
2.2%
Other values (23) 23
25.8%

Length

2024-03-14T11:50:54.922792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
임야 24
21.6%
녹지 19
17.1%
도로 13
11.7%
7
 
6.3%
야영장 6
 
5.4%
잡종지 5
 
4.5%
주차장 4
 
3.6%
천 3
 
2.7%
나대지 3
 
2.7%
대지 3
 
2.7%
Other values (19) 24
21.6%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
-
87 
12년중 사용허가 신청
 
1
12.4.13무주군에분할매각(83㎡),당초 5346㎡
 
1

Length

Max length29
Median length1
Mean length1.4382022
Min length1

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 87
97.8%
12년중 사용허가 신청 1
 
1.1%
12.4.13무주군에분할매각(83㎡),당초 5346㎡ 1
 
1.1%

Length

2024-03-14T11:50:55.020729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:50:55.144227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
87
94.6%
12년중 1
 
1.1%
사용허가 1
 
1.1%
신청 1
 
1.1%
12.4.13무주군에분할매각(83㎡),당초 1
 
1.1%
5346㎡ 1
 
1.1%

Correlations

2024-03-14T11:50:55.220630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분재산분류재산분류.1재산의소재재산의소재.1재산의소재.2재산의소재.3면적(㎡)지목지목.1공시지가(㎡/원)재산가격사용허가면적(㎡)사용허가면적(㎡) .1사용허가면적(㎡) .2용도지구취득일자비고(실제용도)비고
구분1.0001.0001.0000.9390.9440.0000.7830.9680.9040.6730.0000.9420.9670.9650.8530.9330.4920.5080.000
재산분류1.0001.0001.0001.0000.9421.0000.8631.0001.0001.0000.0000.0000.9510.9350.9880.9290.7260.6220.000
재산분류.11.0001.0001.0001.0000.9421.0000.8631.0001.0001.0000.0000.0000.9510.9350.9880.9290.7260.6220.000
재산의소재0.9391.0001.0001.0000.8280.8360.0000.9960.8200.8930.0000.0000.9060.0000.8811.0000.8880.7240.000
재산의소재.10.9440.9420.9420.8281.0000.9970.8641.0000.8810.9310.1660.0000.8900.8870.9400.6200.6910.0000.000
재산의소재.20.0001.0001.0000.8360.9971.0000.0000.9930.9250.9850.9890.3310.0000.0000.7540.8350.9880.9680.000
재산의소재.30.7830.8630.8630.0000.8640.0001.0000.0000.7670.4020.0000.8050.8960.9610.0000.1040.7730.0000.000
면적(㎡)0.9681.0001.0000.9961.0000.9930.0001.0000.8910.9780.9990.9791.0001.0001.0001.0000.9220.9491.000
지목0.9041.0001.0000.8200.8810.9250.7670.8911.0000.9450.6870.0000.6910.0000.7350.9140.8980.9370.579
지목.10.6731.0001.0000.8930.9310.9850.4020.9780.9451.0000.8940.0000.7630.8900.7720.9340.9570.9870.230
공시지가(㎡/원)0.0000.0000.0000.0000.1660.9890.0000.9990.6870.8941.0000.9760.0000.0000.7060.0000.9520.8890.000
재산가격0.9420.0000.0000.0000.0000.3310.8050.9790.0000.0000.9761.0000.9940.9950.0000.0000.0000.9861.000
사용허가면적(㎡)0.9670.9510.9510.9060.8900.0000.8961.0000.6910.7630.0000.9941.0001.0001.0000.0000.9620.9751.000
사용허가면적(㎡) .10.9650.9350.9350.0000.8870.0000.9611.0000.0000.8900.0000.9951.0001.0000.0000.0000.9580.9491.000
사용허가면적(㎡) .20.8530.9880.9880.8810.9400.7540.0001.0000.7350.7720.7060.0001.0000.0001.0000.8020.0000.6090.000
용도지구0.9330.9290.9291.0000.6200.8350.1041.0000.9140.9340.0000.0000.0000.0000.8021.0000.9280.9300.000
취득일자0.4920.7260.7260.8880.6910.9880.7730.9220.8980.9570.9520.0000.9620.9580.0000.9281.0000.9380.828
비고(실제용도)0.5080.6220.6220.7240.0000.9680.0000.9490.9370.9870.8890.9860.9750.9490.6090.9300.9381.0000.949
비고0.0000.0000.0000.0000.0000.0000.0001.0000.5790.2300.0001.0001.0001.0000.0000.0000.8280.9491.000
2024-03-14T11:50:55.371617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재산분류.1재산의소재재산의소재.1지목.1사용허가면적(㎡) .2재산분류용도지구재산의소재.3취득일자비고(실제용도)구분비고공시지가(㎡/원)지목
재산분류.11.0000.9520.7040.8760.8061.0000.6740.6610.3480.2890.7230.0000.0000.940
재산의소재0.9521.0000.6840.5340.5820.9520.9520.0000.4360.2520.4710.0000.0000.502
재산의소재.10.7040.6841.0000.7220.6800.7040.2870.6640.3210.0000.5880.0000.0000.737
지목.10.8760.5340.7221.0000.3360.8760.7260.1090.5250.7480.1550.0920.3930.653
사용허가면적(㎡) .20.8060.5820.6800.3361.0000.8060.4940.0000.0000.1810.3250.0000.2300.365
재산분류1.0000.9520.7040.8760.8061.0000.6740.6610.3480.2890.7230.0000.0000.940
용도지구0.6740.9520.2870.7260.4940.6741.0000.0220.5590.5940.5720.0000.0000.793
재산의소재.30.6610.0000.6640.1090.0000.6610.0221.0000.2580.0000.2470.0000.0000.381
취득일자0.3480.4360.3210.5250.0000.3480.5590.2581.0000.4480.0370.4400.4920.395
비고(실제용도)0.2890.2520.0000.7480.1810.2890.5940.0000.4481.0000.0540.6260.3720.556
구분0.7230.4710.5880.1550.3250.7230.5720.2470.0370.0541.0000.0000.0000.418
비고0.0000.0000.0000.0920.0000.0000.0000.0000.4400.6260.0001.0000.0000.374
공시지가(㎡/원)0.0000.0000.0000.3930.2300.0000.0000.0000.4920.3720.0000.0001.0000.232
지목0.9400.5020.7370.6530.3650.9400.7930.3810.3950.5560.4180.3740.2321.000
2024-03-14T11:50:55.518645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분재산분류재산분류.1재산의소재재산의소재.1재산의소재.3지목지목.1공시지가(㎡/원)사용허가면적(㎡) .2용도지구취득일자비고(실제용도)비고
구분1.0000.7230.7230.4710.5880.2470.4180.1550.0000.3250.5720.0370.0540.000
재산분류0.7231.0001.0000.9520.7040.6610.9400.8760.0000.8060.6740.3480.2890.000
재산분류.10.7231.0001.0000.9520.7040.6610.9400.8760.0000.8060.6740.3480.2890.000
재산의소재0.4710.9520.9521.0000.6840.0000.5020.5340.0000.5820.9520.4360.2520.000
재산의소재.10.5880.7040.7040.6841.0000.6640.7370.7220.0000.6800.2870.3210.0000.000
재산의소재.30.2470.6610.6610.0000.6641.0000.3810.1090.0000.0000.0220.2580.0000.000
지목0.4180.9400.9400.5020.7370.3811.0000.6530.2320.3650.7930.3950.5560.374
지목.10.1550.8760.8760.5340.7220.1090.6531.0000.3930.3360.7260.5250.7480.092
공시지가(㎡/원)0.0000.0000.0000.0000.0000.0000.2320.3931.0000.2300.0000.4920.3720.000
사용허가면적(㎡) .20.3250.8060.8060.5820.6800.0000.3650.3360.2301.0000.4940.0000.1810.000
용도지구0.5720.6740.6740.9520.2870.0220.7930.7260.0000.4941.0000.5590.5940.000
취득일자0.0370.3480.3480.4360.3210.2580.3950.5250.4920.0000.5591.0000.4480.440
비고(실제용도)0.0540.2890.2890.2520.0000.0000.5560.7480.3720.1810.5940.4481.0000.626
비고0.0000.0000.0000.0000.0000.0000.3740.0920.0000.0000.0000.4400.6261.000

Missing values

2024-03-14T11:50:49.836384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:50:50.007739image/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

구분재산분류재산분류.1재산의소재재산의소재.1재산의소재.2재산의소재.3면적(㎡)지목지목.1공시지가(㎡/원)재산가격사용허가면적(㎡)사용허가면적(㎡) .1사용허가면적(㎡) .2용도지구취득일자비고(실제용도)비고
0연번중소위치특수지본번부번-공부현황--계무상유상----
1계--83-필지-165,238---911,443,21091,75566,89824,857----
2지리산--19---124,116---441,525,47951,19533,76617,429----
33행정재산공공용남원시 산내면 덕동리-287-16,759전주차장,궁터2,45030,000,00016,7596,58210,177자연환경1987-09-01달궁터,주차장-
44행정재산공공용남원시 산내면 덕동리-300-804전궁터2,45070,000,000804804-자연환경1987-09-01달궁터-
55행정재산공공용남원시 산내면 덕동리-822736답주차장3,360133,952736736-자연환경1986-04-15덕동야영장 급수탱크12년중 사용허가 신청
61행정재산공공용남원시 산내면 덕동리산10424,664임야임야305324,6604,6644,664-자연환경1987-12-29달궁야영장 급수탱크-
72행정재산공공용남원시 산내면 덕동리산10725,239임야주차장3,360104,7805,239-5,239자연환경1986-10-03달궁야영장 주차장-
811행정재산공공용남원시 산내면 부운리-214-22전잡종지2,3701,958미사용--자연환경1983-12-30임야-
912행정재산공공용남원시 산내면 부운리-216417,240잡종지잡종지90,400283,131,65017,24017,240-자연환경1983-12-30잡종지-
구분재산분류재산분류.1재산의소재재산의소재.1재산의소재.2재산의소재.3면적(㎡)지목지목.1공시지가(㎡/원)재산가격사용허가면적(㎡)사용허가면적(㎡) .1사용허가면적(㎡) .2용도지구취득일자비고(실제용도)비고
7918행정재산공공용무주군 설천면 삼공리-880333임야녹지-359,7003333-자연환경1979-07-23녹지-
8019행정재산공공용무주군 설천면 삼공리-880413임야녹지-141,7001313-자연환경1979-07-24녹지-
8120행정재산공공용무주군 설천면 삼공리-881-982잡종지녹지-703,800982982-자연환경1979-07-23잡종지-
8221행정재산공공용무주군 설천면 삼공리-88211,527잡종지녹지-644,3001,5271,527-자연환경1979-07-23잡종지-
8322행정재산공공용무주군 설천면 삼공리-8822202임야녹지-2,201,800202202-자연환경1979-07-23잡종지-
8423행정재산공공용무주군 설천면 삼공리-8823155임야녹지-1,689,500155155-자연환경1979-07-23잡종지-
8524행정재산공공용무주군 설천면 삼공리-411-20,874주차장주차장133,0003,966,060,00020,874-20,874자연환경1977-12-30주차장-
8625행정재산공공용무주군 설천면 삼공리-41185,263대지대지-980,000,0005,2635,263-자연환경1977-12-30대지12.4.13무주군에분할매각(83㎡),당초 5346㎡
87변산반도--1---562---2,731,320-------
881행정재산공공용부안군 변산면 격포리-5142562임야임야4,8602,731,320----1948-10-02--