Overview

Dataset statistics

Number of variables7
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory60.3 B

Variable types

Text3
Categorical4

Dataset

Description도로, 철도, 주택단지, 산업단지 등 공공개발지 개발과정에서 발생하는 입목을 벌채하여 감정평가 후 공개매각한 기관, 수량, 입찰여부, 낙찰금액을 제공하는 정보
Author산림청
URLhttps://www.data.go.kr/data/15093855/fileData.do

Alerts

매각방식 has constant value ""Constant
비고 is highly overall correlated with 주관기관 and 1 other fieldsHigh correlation
낙찰여부 is highly overall correlated with 비고High correlation
주관기관 is highly overall correlated with 비고High correlation
낙찰여부 is highly imbalanced (54.1%)Imbalance
비고 is highly imbalanced (54.1%)Imbalance
공공개발지 has unique valuesUnique
대상물량(톤) has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:35:47.493242
Analysis finished2023-12-12 01:35:47.959911
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공공개발지
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-12T10:35:48.118329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length8.6451613
Min length5

Characters and Unicode

Total characters268
Distinct characters81
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row오남-수동
2nd row안성-용인 3공구
3rd row새만금-전주
4th row도화-송학
5th row충청내륙 3공구
ValueCountFrequency (%)
안성-용인 7
 
12.7%
2공구 3
 
5.5%
3공구 3
 
5.5%
충청내륙 3
 
5.5%
포항-안동 2
 
3.6%
용상-교리 2
 
3.6%
안성-구리 2
 
3.6%
1 2
 
3.6%
14공구 1
 
1.8%
장흥-광적 1
 
1.8%
Other values (29) 29
52.7%
2023-12-12T10:35:48.445204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 28
 
10.4%
24
 
9.0%
구 18
 
6.7%
공 17
 
6.3%
2 12
 
4.5%
안 11
 
4.1%
성 9
 
3.4%
용 9
 
3.4%
인 7
 
2.6%
1 6
 
2.2%
Other values (71) 127
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 172
64.2%
Decimal Number 32
 
11.9%
Dash Punctuation 28
 
10.4%
Space Separator 24
 
9.0%
Open Punctuation 4
 
1.5%
Close Punctuation 4
 
1.5%
Other Punctuation 3
 
1.1%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 18
 
10.5%
공 17
 
9.9%
안 11
 
6.4%
성 9
 
5.2%
용 9
 
5.2%
인 7
 
4.1%
동 5
 
2.9%
주 4
 
2.3%
남 4
 
2.3%
차 4
 
2.3%
Other values (56) 84
48.8%
Decimal Number
ValueCountFrequency (%)
2 12
37.5%
1 6
18.8%
3 4
 
12.5%
6 3
 
9.4%
4 2
 
6.2%
7 2
 
6.2%
5 1
 
3.1%
8 1
 
3.1%
9 1
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Math Symbol
ValueCountFrequency (%)
∼ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 172
64.2%
Common 96
35.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 18
 
10.5%
공 17
 
9.9%
안 11
 
6.4%
성 9
 
5.2%
용 9
 
5.2%
인 7
 
4.1%
동 5
 
2.9%
주 4
 
2.3%
남 4
 
2.3%
차 4
 
2.3%
Other values (56) 84
48.8%
Common
ValueCountFrequency (%)
- 28
29.2%
24
25.0%
2 12
12.5%
1 6
 
6.2%
( 4
 
4.2%
) 4
 
4.2%
3 4
 
4.2%
6 3
 
3.1%
, 3
 
3.1%
4 2
 
2.1%
Other values (5) 6
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 172
64.2%
ASCII 95
35.4%
Math Operators 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 28
29.5%
24
25.3%
2 12
12.6%
1 6
 
6.3%
( 4
 
4.2%
) 4
 
4.2%
3 4
 
4.2%
6 3
 
3.2%
, 3
 
3.2%
4 2
 
2.1%
Other values (4) 5
 
5.3%
Hangul
ValueCountFrequency (%)
구 18
 
10.5%
공 17
 
9.9%
안 11
 
6.4%
성 9
 
5.2%
용 9
 
5.2%
인 7
 
4.1%
동 5
 
2.9%
주 4
 
2.3%
남 4
 
2.3%
차 4
 
2.3%
Other values (56) 84
48.8%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%

주관기관
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Memory size380.0 B
한국도로공사 안성용인건설사업단
7 
대전지방국토관리청
6 
경기도 건설본부
4 
부산지방국토관리청
4 
익산지방국토관리청
2 
Other values (7)
8 

Length

Max length17
Median length16
Mean length11.677419
Min length8

Unique

Unique6 ?
Unique (%)19.4%

Sample

1st row경기도 건설본부
2nd row한국도로공사 안성용인건설사업단
3rd row한국도로공사 새만금전주건설사업단
4th row대전지방국토관리청
5th row대전지방국토관리청

Common Values

ValueCountFrequency (%)
한국도로공사 안성용인건설사업단 7
22.6%
대전지방국토관리청 6
19.4%
경기도 건설본부 4
12.9%
부산지방국토관리청 4
12.9%
익산지방국토관리청 2
 
6.5%
한국도로공사 용인구리건설사업단 2
 
6.5%
한국도로공사 새만금전주건설사업단 1
 
3.2%
한국도로공사 함천창녕건설사업단 1
 
3.2%
한국도로공사 함양합천건설사업단 1
 
3.2%
국가보훈처 제주보훈청 1
 
3.2%
Other values (2) 2
 
6.5%

Length

2023-12-12T10:35:48.637170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한국도로공사 12
25.0%
안성용인건설사업단 7
14.6%
대전지방국토관리청 6
12.5%
경기도 4
 
8.3%
건설본부 4
 
8.3%
부산지방국토관리청 4
 
8.3%
익산지방국토관리청 2
 
4.2%
용인구리건설사업단 2
 
4.2%
새만금전주건설사업단 1
 
2.1%
함천창녕건설사업단 1
 
2.1%
Other values (5) 5
10.4%

매각방식
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
원목매각
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원목매각 31
100.0%

Length

2023-12-12T10:35:48.797877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:35:48.912682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원목매각 31
100.0%

대상물량(톤)
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-12T10:35:49.110780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.8064516
Min length2

Characters and Unicode

Total characters118
Distinct characters12
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

Unique31 ?
Unique (%)100.0%

Sample

1st row216.31
2nd row500
3rd row4,581
4th row160
5th row52
ValueCountFrequency (%)
216.31 1
 
3.2%
254 1
 
3.2%
1,200 1
 
3.2%
625 1
 
3.2%
80 1
 
3.2%
560 1
 
3.2%
205.2 1
 
3.2%
225.2 1
 
3.2%
20.64 1
 
3.2%
400 1
 
3.2%
Other values (21) 21
67.7%
2023-12-12T10:35:49.538385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 17
14.4%
1 17
14.4%
0 16
13.6%
5 14
11.9%
6 12
10.2%
4 9
7.6%
. 8
6.8%
3 7
5.9%
7 7
5.9%
8 6
 
5.1%
Other values (2) 5
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107
90.7%
Other Punctuation 11
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17
15.9%
1 17
15.9%
0 16
15.0%
5 14
13.1%
6 12
11.2%
4 9
8.4%
3 7
6.5%
7 7
6.5%
8 6
 
5.6%
9 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 8
72.7%
, 3
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Common 118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 17
14.4%
1 17
14.4%
0 16
13.6%
5 14
11.9%
6 12
10.2%
4 9
7.6%
. 8
6.8%
3 7
5.9%
7 7
5.9%
8 6
 
5.1%
Other values (2) 5
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 17
14.4%
1 17
14.4%
0 16
13.6%
5 14
11.9%
6 12
10.2%
4 9
7.6%
. 8
6.8%
3 7
5.9%
7 7
5.9%
8 6
 
5.1%
Other values (2) 5
 
4.2%

낙찰여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
유
28 
무
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유 28
90.3%
무 3
 
9.7%

Length

2023-12-12T10:35:49.723504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:35:49.857934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유 28
90.3%
무 3
 
9.7%

낙찰금액(원)
Text

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-12T10:35:50.060481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.8064516
Min length1

Characters and Unicode

Total characters273
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

Unique28 ?
Unique (%)90.3%

Sample

1st row6,812,200
2nd row25,000,000
3rd row197,010,000
4th row6,199,000
5th row1,887,600
ValueCountFrequency (%)
0 3
 
9.7%
6,812,200 1
 
3.2%
69,015,000 1
 
3.2%
50,040,000 1
 
3.2%
17,312,500 1
 
3.2%
2,770,000 1
 
3.2%
8,228,520 1
 
3.2%
10,150,000 1
 
3.2%
535,000 1
 
3.2%
19,800,000 1
 
3.2%
Other values (19) 19
61.3%
2023-12-12T10:35:50.460781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 101
37.0%
, 55
20.1%
1 21
 
7.7%
5 20
 
7.3%
2 17
 
6.2%
8 15
 
5.5%
9 13
 
4.8%
7 12
 
4.4%
6 11
 
4.0%
4 5
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 218
79.9%
Other Punctuation 55
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101
46.3%
1 21
 
9.6%
5 20
 
9.2%
2 17
 
7.8%
8 15
 
6.9%
9 13
 
6.0%
7 12
 
5.5%
6 11
 
5.0%
4 5
 
2.3%
3 3
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 273
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101
37.0%
, 55
20.1%
1 21
 
7.7%
5 20
 
7.3%
2 17
 
6.2%
8 15
 
5.5%
9 13
 
4.8%
7 12
 
4.4%
6 11
 
4.0%
4 5
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101
37.0%
, 55
20.1%
1 21
 
7.7%
5 20
 
7.3%
2 17
 
6.2%
8 15
 
5.5%
9 13
 
4.8%
7 12
 
4.4%
6 11
 
4.0%
4 5
 
1.8%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
28 
유찰 2회
3 

Length

Max length5
Median length4
Mean length4.0967742
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
90.3%
유찰 2회 3
 
9.7%

Length

2023-12-12T10:35:50.646127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:35:50.794995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
82.4%
유찰 3
 
8.8%
2회 3
 
8.8%

Correlations

2023-12-12T10:35:51.213349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공공개발지주관기관대상물량(톤)낙찰여부낙찰금액(원)
공공개발지1.0001.0001.0001.0001.000
주관기관1.0001.0001.0000.4840.605
대상물량(톤)1.0001.0001.0001.0001.000
낙찰여부1.0000.4841.0001.0001.000
낙찰금액(원)1.0000.6051.0001.0001.000
2023-12-12T10:35:51.353480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고낙찰여부주관기관
비고1.0001.0001.000
낙찰여부1.0001.0000.287
주관기관1.0000.2871.000
2023-12-12T10:35:51.493601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주관기관낙찰여부비고
주관기관1.0000.2871.000
낙찰여부0.2871.0001.000
비고1.0001.0001.000

Missing values

2023-12-12T10:35:47.790717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:35:47.904192image/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

공공개발지주관기관매각방식대상물량(톤)낙찰여부낙찰금액(원)비고
0오남-수동경기도 건설본부원목매각216.31유6,812,200<NA>
1안성-용인 3공구한국도로공사 안성용인건설사업단원목매각500유25,000,000<NA>
2새만금-전주한국도로공사 새만금전주건설사업단원목매각4,581유197,010,000<NA>
3도화-송학대전지방국토관리청원목매각160유6,199,000<NA>
4충청내륙 3공구대전지방국토관리청원목매각52유1,887,600<NA>
5안성-용인 6공구한국도로공사 안성용인건설사업단원목매각556유22,900,000<NA>
6오남-수동(2차)경기도 건설본부원목매각373유11,284,000<NA>
7단양-영월대전지방국토관리청원목매각123유4,515,050<NA>
8합천-창녕 7, 8공구한국도로공사 함천창녕건설사업단원목매각321무0유찰 2회
9안성-용인 1공구한국도로공사 안성용인건설사업단원목매각1410유57,810,000<NA>
공공개발지주관기관매각방식대상물량(톤)낙찰여부낙찰금액(원)비고
21안성-용인 3공구(2차)한국도로공사 안성용인건설사업단원목매각800유36,800,000<NA>
22제주국립묘지국가보훈처 제주보훈청원목매각400유19,800,000<NA>
23남일-보은 2공구대전지방국토관리청원목매각20.64유535,000<NA>
24춘천-화천 1원주지방국토관리청원목매각225.2유10,150,000<NA>
25충청내륙 2공구대전지방국토관리청원목매각205.2유8,228,520<NA>
26장흥-광적경기도 건설본부원목매각560무0유찰 2회
27안성-구리 14공구한국도로공사 용인구리건설사업단원목매각80유2,770,000<NA>
28남부권역 시거장애목 정비공사논산국토관리사무소원목매각625유17,312,500<NA>
29용상-교리 2부산지방국토관리청원목매각1,200유50,040,000<NA>
30충청내륙 2공구(2차)대전지방국토관리청원목매각478유19,990,000<NA>