Overview

Dataset statistics

Number of variables19
Number of observations84
Missing cells150
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 KiB
Average record size in memory157.6 B

Variable types

Numeric4
Categorical5
Text6
Boolean3
DateTime1

Dataset

Description중소벤처기업진흥공단에서 운영중인 자산거래중개장터에 등록된 공장 정보입니다. 데이터 발생 된 시점부터 추출했습니다.
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15069354/fileData.do

Alerts

분류 has constant value ""Constant
용지면적 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 용지면적 and 1 other fieldsHigh correlation
메인노출여부 is highly overall correlated with 연면적High correlation
가격협의가능 is highly imbalanced (72.4%)Imbalance
메인노출여부 is highly imbalanced (90.7%)Imbalance
키워드2 has 20 (23.8%) missing valuesMissing
키워드3 has 31 (36.9%) missing valuesMissing
키워드4 has 48 (57.1%) missing valuesMissing
연면적 has 29 (34.5%) missing valuesMissing
기계포함여부 has 22 (26.2%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:30:09.241782
Analysis finished2023-12-12 23:30:12.720651
Duration3.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5819.8571
Minimum142
Maximum10976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:30:12.789462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum142
5-th percentile266.8
Q13296.25
median5474.5
Q38901.75
95-th percentile10553.85
Maximum10976
Range10834
Interquartile range (IQR)5605.5

Descriptive statistics

Standard deviation3260.7284
Coefficient of variation (CV)0.56027636
Kurtosis-1.0958358
Mean5819.8571
Median Absolute Deviation (MAD)3123.5
Skewness-0.059914885
Sum488868
Variance10632349
MonotonicityStrictly decreasing
2023-12-13T08:30:12.921211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10976 1
 
1.2%
4589 1
 
1.2%
3312 1
 
1.2%
3345 1
 
1.2%
3346 1
 
1.2%
3864 1
 
1.2%
4129 1
 
1.2%
4150 1
 
1.2%
4181 1
 
1.2%
4467 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
142 1
1.2%
151 1
1.2%
250 1
1.2%
257 1
1.2%
259 1
1.2%
311 1
1.2%
337 1
1.2%
338 1
1.2%
1205 1
1.2%
1339 1
1.2%
ValueCountFrequency (%)
10976 1
1.2%
10965 1
1.2%
10806 1
1.2%
10558 1
1.2%
10557 1
1.2%
10536 1
1.2%
10366 1
1.2%
10365 1
1.2%
10273 1
1.2%
10272 1
1.2%

분류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
공장
84 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공장 84
100.0%

Length

2023-12-13T08:30:13.031392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:13.126597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공장 84
100.0%

공장명
Text

Distinct71
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-13T08:30:13.300914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length30
Mean length9
Min length2

Characters and Unicode

Total characters756
Distinct characters211
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)71.4%

Sample

1st row예봉엔씨피
2nd rowHIEO(주) 공장
3rd row(주)테크랜드
4th row해락원영어조합법인
5th row테스트
ValueCountFrequency (%)
공장 8
 
5.5%
달천공장 4
 
2.8%
매매 2
 
1.4%
제조공장 2
 
1.4%
주)흥국 2
 
1.4%
및 2
 
1.4%
공장부지 2
 
1.4%
주)케이엠 2
 
1.4%
가인 2
 
1.4%
자동차부품공장 2
 
1.4%
Other values (109) 117
80.7%
2023-12-13T08:30:13.663477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
 
8.1%
공 46
 
6.1%
장 43
 
5.7%
주 26
 
3.4%
) 23
 
3.0%
( 23
 
3.0%
스 13
 
1.7%
지 11
 
1.5%
산 9
 
1.2%
부 9
 
1.2%
Other values (201) 492
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 600
79.4%
Space Separator 61
 
8.1%
Close Punctuation 24
 
3.2%
Open Punctuation 24
 
3.2%
Decimal Number 19
 
2.5%
Uppercase Letter 11
 
1.5%
Lowercase Letter 8
 
1.1%
Other Punctuation 7
 
0.9%
Math Symbol 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 46
 
7.7%
장 43
 
7.2%
주 26
 
4.3%
스 13
 
2.2%
지 11
 
1.8%
산 9
 
1.5%
부 9
 
1.5%
업 8
 
1.3%
가 8
 
1.3%
트 8
 
1.3%
Other values (172) 419
69.8%
Decimal Number
ValueCountFrequency (%)
0 6
31.6%
1 4
21.1%
5 3
15.8%
2 2
 
10.5%
4 2
 
10.5%
3 1
 
5.3%
8 1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
I 3
27.3%
E 2
18.2%
C 2
18.2%
T 1
 
9.1%
A 1
 
9.1%
H 1
 
9.1%
O 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
& 3
42.9%
; 2
28.6%
, 1
 
14.3%
. 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
o 2
25.0%
t 2
25.0%
u 2
25.0%
q 2
25.0%
Close Punctuation
ValueCountFrequency (%)
) 23
95.8%
] 1
 
4.2%
Open Punctuation
ValueCountFrequency (%)
( 23
95.8%
[ 1
 
4.2%
Space Separator
ValueCountFrequency (%)
61
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 600
79.4%
Common 137
 
18.1%
Latin 19
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 46
 
7.7%
장 43
 
7.2%
주 26
 
4.3%
스 13
 
2.2%
지 11
 
1.8%
산 9
 
1.5%
부 9
 
1.5%
업 8
 
1.3%
가 8
 
1.3%
트 8
 
1.3%
Other values (172) 419
69.8%
Common
ValueCountFrequency (%)
61
44.5%
) 23
 
16.8%
( 23
 
16.8%
0 6
 
4.4%
1 4
 
2.9%
5 3
 
2.2%
& 3
 
2.2%
; 2
 
1.5%
2 2
 
1.5%
4 2
 
1.5%
Other values (8) 8
 
5.8%
Latin
ValueCountFrequency (%)
I 3
15.8%
E 2
10.5%
C 2
10.5%
o 2
10.5%
t 2
10.5%
u 2
10.5%
q 2
10.5%
T 1
 
5.3%
A 1
 
5.3%
H 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 600
79.4%
ASCII 156
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
39.1%
) 23
 
14.7%
( 23
 
14.7%
0 6
 
3.8%
1 4
 
2.6%
5 3
 
1.9%
I 3
 
1.9%
& 3
 
1.9%
E 2
 
1.3%
; 2
 
1.3%
Other values (19) 26
16.7%
Hangul
ValueCountFrequency (%)
공 46
 
7.7%
장 43
 
7.2%
주 26
 
4.3%
스 13
 
2.2%
지 11
 
1.8%
산 9
 
1.5%
부 9
 
1.5%
업 8
 
1.3%
가 8
 
1.3%
트 8
 
1.3%
Other values (172) 419
69.8%

키워드1
Text

Distinct55
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-13T08:30:13.905202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.6190476
Min length1

Characters and Unicode

Total characters304
Distinct characters101
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)51.2%

Sample

1st row일반공장
2nd row제조공장
3rd row공장
4th row조미김 가공 공장
5th row테스트
ValueCountFrequency (%)
공장 14
 
15.2%
일반공장 5
 
5.4%
공장매매 5
 
5.4%
제조공장 4
 
4.3%
공장매각 3
 
3.3%
아파트형공장 3
 
3.3%
제조 2
 
2.2%
강원 2
 
2.2%
매매 2
 
2.2%
식품 2
 
2.2%
Other values (48) 50
54.3%
2023-12-13T08:30:14.511522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 46
 
15.1%
장 44
 
14.5%
매 17
 
5.6%
조 9
 
3.0%
8
 
2.6%
제 7
 
2.3%
트 6
 
2.0%
가 6
 
2.0%
일 6
 
2.0%
반 5
 
1.6%
Other values (91) 150
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 289
95.1%
Space Separator 8
 
2.6%
Decimal Number 5
 
1.6%
Uppercase Letter 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 46
 
15.9%
장 44
 
15.2%
매 17
 
5.9%
조 9
 
3.1%
제 7
 
2.4%
트 6
 
2.1%
가 6
 
2.1%
일 6
 
2.1%
반 5
 
1.7%
지 4
 
1.4%
Other values (85) 139
48.1%
Decimal Number
ValueCountFrequency (%)
3 2
40.0%
1 2
40.0%
2 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 289
95.1%
Common 13
 
4.3%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 46
 
15.9%
장 44
 
15.2%
매 17
 
5.9%
조 9
 
3.1%
제 7
 
2.4%
트 6
 
2.1%
가 6
 
2.1%
일 6
 
2.1%
반 5
 
1.7%
지 4
 
1.4%
Other values (85) 139
48.1%
Common
ValueCountFrequency (%)
8
61.5%
3 2
 
15.4%
1 2
 
15.4%
2 1
 
7.7%
Latin
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 289
95.1%
ASCII 15
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 46
 
15.9%
장 44
 
15.2%
매 17
 
5.9%
조 9
 
3.1%
제 7
 
2.4%
트 6
 
2.1%
가 6
 
2.1%
일 6
 
2.1%
반 5
 
1.7%
지 4
 
1.4%
Other values (85) 139
48.1%
ASCII
ValueCountFrequency (%)
8
53.3%
3 2
 
13.3%
1 2
 
13.3%
T 1
 
6.7%
I 1
 
6.7%
2 1
 
6.7%

키워드2
Text

MISSING 

Distinct49
Distinct (%)76.6%
Missing20
Missing (%)23.8%
Memory size804.0 B
2023-12-13T08:30:14.769736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4
Min length2

Characters and Unicode

Total characters256
Distinct characters102
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

Unique41 ?
Unique (%)64.1%

Sample

1st row의약품공장
2nd row평택공장
3rd row임대
4th row공장
5th row공장부지
ValueCountFrequency (%)
공장 9
 
13.0%
충주공장 3
 
4.3%
창고 3
 
4.3%
1500평 3
 
4.3%
공장부지 3
 
4.3%
원주 2
 
2.9%
김치절임 2
 
2.9%
제조공장 2
 
2.9%
군포아파트형공장 1
 
1.4%
광고기획사 1
 
1.4%
Other values (40) 40
58.0%
2023-12-13T08:30:15.121279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 28
 
10.9%
장 27
 
10.5%
0 10
 
3.9%
평 8
 
3.1%
지 7
 
2.7%
부 6
 
2.3%
5 5
 
2.0%
매 5
 
2.0%
5
 
2.0%
1 5
 
2.0%
Other values (92) 150
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 223
87.1%
Decimal Number 27
 
10.5%
Space Separator 5
 
2.0%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 28
 
12.6%
장 27
 
12.1%
평 8
 
3.6%
지 7
 
3.1%
부 6
 
2.7%
매 5
 
2.2%
주 5
 
2.2%
고 5
 
2.2%
원 4
 
1.8%
품 4
 
1.8%
Other values (82) 124
55.6%
Decimal Number
ValueCountFrequency (%)
0 10
37.0%
5 5
18.5%
1 5
18.5%
2 3
 
11.1%
4 1
 
3.7%
8 1
 
3.7%
3 1
 
3.7%
6 1
 
3.7%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 223
87.1%
Common 33
 
12.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 28
 
12.6%
장 27
 
12.1%
평 8
 
3.6%
지 7
 
3.1%
부 6
 
2.7%
매 5
 
2.2%
주 5
 
2.2%
고 5
 
2.2%
원 4
 
1.8%
품 4
 
1.8%
Other values (82) 124
55.6%
Common
ValueCountFrequency (%)
0 10
30.3%
5 5
15.2%
5
15.2%
1 5
15.2%
2 3
 
9.1%
4 1
 
3.0%
. 1
 
3.0%
8 1
 
3.0%
3 1
 
3.0%
6 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 223
87.1%
ASCII 33
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 28
 
12.6%
장 27
 
12.1%
평 8
 
3.6%
지 7
 
3.1%
부 6
 
2.7%
매 5
 
2.2%
주 5
 
2.2%
고 5
 
2.2%
원 4
 
1.8%
품 4
 
1.8%
Other values (82) 124
55.6%
ASCII
ValueCountFrequency (%)
0 10
30.3%
5 5
15.2%
5
15.2%
1 5
15.2%
2 3
 
9.1%
4 1
 
3.0%
. 1
 
3.0%
8 1
 
3.0%
3 1
 
3.0%
6 1
 
3.0%

키워드3
Text

MISSING 

Distinct43
Distinct (%)81.1%
Missing31
Missing (%)36.9%
Memory size804.0 B
2023-12-13T08:30:15.379323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length3.9056604
Min length2

Characters and Unicode

Total characters207
Distinct characters104
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)67.9%

Sample

1st row식품공장
2nd row교통편리
3rd row토지
4th row토지
5th row공장
ValueCountFrequency (%)
공장 5
 
9.3%
제조공장 3
 
5.6%
자동차 2
 
3.7%
토지 2
 
3.7%
경기도 2
 
3.7%
산업단지 2
 
3.7%
냉동냉장 2
 
3.7%
경주게스트하우스리모델링 1
 
1.9%
공장매각 1
 
1.9%
우림라이온스벨리5차 1
 
1.9%
Other values (33) 33
61.1%
2023-12-13T08:30:15.758300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 18
 
8.7%
장 17
 
8.2%
동 6
 
2.9%
기 6
 
2.9%
매 6
 
2.9%
지 5
 
2.4%
경 5
 
2.4%
조 4
 
1.9%
스 4
 
1.9%
리 4
 
1.9%
Other values (94) 132
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 196
94.7%
Decimal Number 5
 
2.4%
Uppercase Letter 4
 
1.9%
Other Punctuation 1
 
0.5%
Space Separator 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 18
 
9.2%
장 17
 
8.7%
동 6
 
3.1%
기 6
 
3.1%
매 6
 
3.1%
지 5
 
2.6%
경 5
 
2.6%
조 4
 
2.0%
스 4
 
2.0%
리 4
 
2.0%
Other values (85) 121
61.7%
Uppercase Letter
ValueCountFrequency (%)
P 1
25.0%
S 1
25.0%
G 1
25.0%
K 1
25.0%
Decimal Number
ValueCountFrequency (%)
0 2
40.0%
5 2
40.0%
1 1
20.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 196
94.7%
Common 7
 
3.4%
Latin 4
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 18
 
9.2%
장 17
 
8.7%
동 6
 
3.1%
기 6
 
3.1%
매 6
 
3.1%
지 5
 
2.6%
경 5
 
2.6%
조 4
 
2.0%
스 4
 
2.0%
리 4
 
2.0%
Other values (85) 121
61.7%
Common
ValueCountFrequency (%)
0 2
28.6%
5 2
28.6%
& 1
14.3%
1
14.3%
1 1
14.3%
Latin
ValueCountFrequency (%)
P 1
25.0%
S 1
25.0%
G 1
25.0%
K 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 196
94.7%
ASCII 11
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 18
 
9.2%
장 17
 
8.7%
동 6
 
3.1%
기 6
 
3.1%
매 6
 
3.1%
지 5
 
2.6%
경 5
 
2.6%
조 4
 
2.0%
스 4
 
2.0%
리 4
 
2.0%
Other values (85) 121
61.7%
ASCII
ValueCountFrequency (%)
0 2
18.2%
5 2
18.2%
& 1
9.1%
P 1
9.1%
1
9.1%
1 1
9.1%
S 1
9.1%
G 1
9.1%
K 1
9.1%

키워드4
Text

MISSING 

Distinct32
Distinct (%)88.9%
Missing48
Missing (%)57.1%
Memory size804.0 B
2023-12-13T08:30:15.999796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.3888889
Min length2

Characters and Unicode

Total characters122
Distinct characters73
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

Unique28 ?
Unique (%)77.8%

Sample

1st row고덕신도시
2nd row일반공장
3rd row일반공장
4th row식품공장
5th row가평
ValueCountFrequency (%)
일반공장 2
 
5.6%
산업단지 2
 
5.6%
창고 2
 
5.6%
농공단지 2
 
5.6%
급속냉동 1
 
2.8%
출력소 1
 
2.8%
안양아파트형공장 1
 
2.8%
다용도리모델링 1
 
2.8%
천막 1
 
2.8%
기구 1
 
2.8%
Other values (22) 22
61.1%
2023-12-13T08:30:16.397730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 11
 
9.0%
장 7
 
5.7%
지 7
 
5.7%
고 5
 
4.1%
단 4
 
3.3%
업 4
 
3.3%
제 3
 
2.5%
급 3
 
2.5%
일 2
 
1.6%
대 2
 
1.6%
Other values (63) 74
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
96.7%
Uppercase Letter 4
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 11
 
9.3%
장 7
 
5.9%
지 7
 
5.9%
고 5
 
4.2%
단 4
 
3.4%
업 4
 
3.4%
제 3
 
2.5%
급 3
 
2.5%
일 2
 
1.7%
대 2
 
1.7%
Other values (59) 70
59.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
25.0%
G 1
25.0%
M 1
25.0%
P 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
96.7%
Latin 4
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 11
 
9.3%
장 7
 
5.9%
지 7
 
5.9%
고 5
 
4.2%
단 4
 
3.4%
업 4
 
3.4%
제 3
 
2.5%
급 3
 
2.5%
일 2
 
1.7%
대 2
 
1.7%
Other values (59) 70
59.3%
Latin
ValueCountFrequency (%)
B 1
25.0%
G 1
25.0%
M 1
25.0%
P 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
96.7%
ASCII 4
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 11
 
9.3%
장 7
 
5.9%
지 7
 
5.9%
고 5
 
4.2%
단 4
 
3.4%
업 4
 
3.4%
제 3
 
2.5%
급 3
 
2.5%
일 2
 
1.7%
대 2
 
1.7%
Other values (59) 70
59.3%
ASCII
ValueCountFrequency (%)
B 1
25.0%
G 1
25.0%
M 1
25.0%
P 1
25.0%

소재지
Text

Distinct55
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-13T08:30:16.684280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.3571429
Min length5

Characters and Unicode

Total characters702
Distinct characters74
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

Unique37 ?
Unique (%)44.0%

Sample

1st row충청북도 음성군
2nd row경기도 평택시
3rd row인천광역시 남구
4th row충청남도 서천군
5th row서울특별시 양천구
ValueCountFrequency (%)
경기도 17
 
9.6%
충청북도 11
 
6.2%
충청남도 11
 
6.2%
서울특별시 9
 
5.1%
경상남도 7
 
4.0%
울산광역시 5
 
2.8%
충주시 5
 
2.8%
경상북도 5
 
2.8%
북구 5
 
2.8%
전라북도 4
 
2.3%
Other values (66) 98
55.4%
2023-12-13T08:30:17.061310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
 
13.2%
시 69
 
9.8%
도 61
 
8.7%
경 33
 
4.7%
구 33
 
4.7%
충 27
 
3.8%
북 26
 
3.7%
남 26
 
3.7%
청 24
 
3.4%
기 19
 
2.7%
Other values (64) 291
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 609
86.8%
Space Separator 93
 
13.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 69
 
11.3%
도 61
 
10.0%
경 33
 
5.4%
구 33
 
5.4%
충 27
 
4.4%
북 26
 
4.3%
남 26
 
4.3%
청 24
 
3.9%
기 19
 
3.1%
군 18
 
3.0%
Other values (63) 273
44.8%
Space Separator
ValueCountFrequency (%)
93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 609
86.8%
Common 93
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 69
 
11.3%
도 61
 
10.0%
경 33
 
5.4%
구 33
 
5.4%
충 27
 
4.4%
북 26
 
4.3%
남 26
 
4.3%
청 24
 
3.9%
기 19
 
3.1%
군 18
 
3.0%
Other values (63) 273
44.8%
Common
ValueCountFrequency (%)
93
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 609
86.8%
ASCII 93
 
13.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
93
100.0%
Hangul
ValueCountFrequency (%)
시 69
 
11.3%
도 61
 
10.0%
경 33
 
5.4%
구 33
 
5.4%
충 27
 
4.4%
북 26
 
4.3%
남 26
 
4.3%
청 24
 
3.9%
기 19
 
3.1%
군 18
 
3.0%
Other values (63) 273
44.8%

용지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20910.265
Minimum1
Maximum1199900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:30:17.228716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.5
Q11645
median3668.65
Q38550
95-th percentile24721
Maximum1199900
Range1199899
Interquartile range (IQR)6905

Descriptive statistics

Standard deviation130518.44
Coefficient of variation (CV)6.2418357
Kurtosis83.121905
Mean20910.265
Median Absolute Deviation (MAD)3139
Skewness9.0951719
Sum1756462.3
Variance1.7035063 × 1010
MonotonicityNot monotonic
2023-12-13T08:30:17.384390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2799.0 3
 
3.6%
4950.0 3
 
3.6%
330.0 2
 
2.4%
12121.0 2
 
2.4%
12280.0 2
 
2.4%
3300.0 2
 
2.4%
1000.0 2
 
2.4%
2174.0 2
 
2.4%
12605.0 2
 
2.4%
191.57 2
 
2.4%
Other values (61) 62
73.8%
ValueCountFrequency (%)
1.0 1
1.2%
3.104 1
1.2%
50.0 1
1.2%
100.0 2
2.4%
110.0 1
1.2%
180.0 1
1.2%
191.57 2
2.4%
247.5 1
1.2%
292.74 1
1.2%
325.4 1
1.2%
ValueCountFrequency (%)
1199900.0 1
1.2%
51578.1 1
1.2%
49500.0 1
1.2%
33543.0 1
1.2%
25000.0 1
1.2%
23140.0 1
1.2%
23000.0 1
1.2%
16352.0 1
1.2%
16000.0 1
1.2%
14587.0 1
1.2%

연면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)90.9%
Missing29
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean6197.3253
Minimum1
Maximum170000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:30:17.526089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile194
Q11130.99
median2248
Q34522.575
95-th percentile8835.992
Maximum170000
Range169999
Interquartile range (IQR)3391.585

Descriptive statistics

Standard deviation22706.763
Coefficient of variation (CV)3.6639617
Kurtosis52.874201
Mean6197.3253
Median Absolute Deviation (MAD)1668
Skewness7.2093767
Sum340852.89
Variance5.1559707 × 108
MonotonicityNot monotonic
2023-12-13T08:30:17.697915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3952.0 3
 
3.6%
2433.0 2
 
2.4%
8508.56 2
 
2.4%
1000.0 2
 
2.4%
1897.12 1
 
1.2%
5489.0 1
 
1.2%
4090.25 1
 
1.2%
264.0 1
 
1.2%
3300.0 1
 
1.2%
3396.0 1
 
1.2%
Other values (40) 40
47.6%
(Missing) 29
34.5%
ValueCountFrequency (%)
1.0 1
1.2%
1.141 1
1.2%
180.0 1
1.2%
200.0 1
1.2%
264.0 1
1.2%
324.48 1
1.2%
460.0 1
1.2%
572.0 1
1.2%
580.0 1
1.2%
825.0 1
1.2%
ValueCountFrequency (%)
170000.0 1
1.2%
17096.0 1
1.2%
9600.0 1
1.2%
8508.56 2
2.4%
7354.88 1
1.2%
7255.59 1
1.2%
6909.0 1
1.2%
6188.48 1
1.2%
5579.37 1
1.2%
5579.0 1
1.2%

가격협의가능
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size804.0 B
가능
80 
불가능
 
4

Length

Max length3
Median length2
Mean length2.047619
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가능 80
95.2%
불가능 4
 
4.8%

Length

2023-12-13T08:30:17.823596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:17.957389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가능 80
95.2%
불가능 4
 
4.8%

공장등록여부
Boolean

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size216.0 B
True
73 
False
11 
ValueCountFrequency (%)
True 73
86.9%
False 11
 
13.1%
2023-12-13T08:30:18.037195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

준공년도
Real number (ℝ)

Distinct23
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.8929
Minimum1983
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:30:18.138826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1990.15
Q11999
median2007
Q32009.25
95-th percentile2013
Maximum2013
Range30
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation7.5010326
Coefficient of variation (CV)0.0037432304
Kurtosis-0.06870599
Mean2003.8929
Median Absolute Deviation (MAD)5
Skewness-0.81698973
Sum168327
Variance56.265491
MonotonicityNot monotonic
2023-12-13T08:30:18.259615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2008 12
14.3%
2013 8
 
9.5%
1995 8
 
9.5%
2009 6
 
7.1%
2000 6
 
7.1%
2011 5
 
6.0%
1996 4
 
4.8%
2004 4
 
4.8%
2010 4
 
4.8%
2007 4
 
4.8%
Other values (13) 23
27.4%
ValueCountFrequency (%)
1983 1
 
1.2%
1985 1
 
1.2%
1986 1
 
1.2%
1987 1
 
1.2%
1990 1
 
1.2%
1991 1
 
1.2%
1994 1
 
1.2%
1995 8
9.5%
1996 4
4.8%
1999 3
 
3.6%
ValueCountFrequency (%)
2013 8
9.5%
2012 4
 
4.8%
2011 5
6.0%
2010 4
 
4.8%
2009 6
7.1%
2008 12
14.3%
2007 4
 
4.8%
2006 2
 
2.4%
2005 2
 
2.4%
2004 4
 
4.8%

용도지역
Categorical

Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size804.0 B
일반공업지역
32 
관리지역
16 
공업지역
14 
기타
6 
준도시지역
5 
Other values (4)
11 

Length

Max length6
Median length5
Mean length4.75
Min length2

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st row일반공업지역
2nd row준도시지역
3rd row일반공업지역
4th row기타
5th row농림지역

Common Values

ValueCountFrequency (%)
일반공업지역 32
38.1%
관리지역 16
19.0%
공업지역 14
16.7%
기타 6
 
7.1%
준도시지역 5
 
6.0%
도시지역 5
 
6.0%
준공업지역 4
 
4.8%
농림지역 1
 
1.2%
전용공업지역 1
 
1.2%

Length

2023-12-13T08:30:18.399235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:18.514049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반공업지역 32
38.1%
관리지역 16
19.0%
공업지역 14
16.7%
기타 6
 
7.1%
준도시지역 5
 
6.0%
도시지역 5
 
6.0%
준공업지역 4
 
4.8%
농림지역 1
 
1.2%
전용공업지역 1
 
1.2%

기계포함여부
Boolean

MISSING 

Distinct2
Distinct (%)3.2%
Missing22
Missing (%)26.2%
Memory size300.0 B
True
35 
False
27 
(Missing)
22 
ValueCountFrequency (%)
True 35
41.7%
False 27
32.1%
(Missing) 22
26.2%
2023-12-13T08:30:18.641709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

게시기간
Date

Distinct64
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
Minimum2013-06-30 00:00:00
Maximum2015-12-14 00:00:00
2023-12-13T08:30:18.749120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:18.895285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

메인노출여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size216.0 B
False
83 
True
 
1
ValueCountFrequency (%)
False 83
98.8%
True 1
 
1.2%
2023-12-13T08:30:19.059160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

진행상태
Categorical

Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
등록
58 
삭제
24 
완료
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록
2nd row등록
3rd row등록
4th row등록
5th row삭제

Common Values

ValueCountFrequency (%)
등록 58
69.0%
삭제 24
28.6%
완료 2
 
2.4%

Length

2023-12-13T08:30:19.182257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:19.278340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록 58
69.0%
삭제 24
28.6%
완료 2
 
2.4%

개인_기업구분
Categorical

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size804.0 B
기업
53 
개인
31 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row기업
3rd row기업
4th row기업
5th row기업

Common Values

ValueCountFrequency (%)
기업 53
63.1%
개인 31
36.9%

Length

2023-12-13T08:30:19.407826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:30:19.514421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기업 53
63.1%
개인 31
36.9%

Interactions

2023-12-13T08:30:11.886201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:10.702191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.098137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.516926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.962057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:10.781075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.199478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.607241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:12.058867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:10.883709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.319836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.701777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:12.146897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:10.990835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.417319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:11.788499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:30:19.593909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호공장명키워드1키워드2키워드3키워드4소재지용지면적연면적가격협의가능공장등록여부준공년도용도지역기계포함여부게시기간메인노출여부진행상태개인_기업구분
번호1.0000.9750.6900.8710.8060.8470.9430.0000.1940.1030.4360.4450.0000.3810.9970.1410.0000.345
공장명0.9751.0000.9960.9870.9970.9950.9991.0001.0001.0001.0000.9970.9990.8720.9951.0000.8620.976
키워드10.6900.9961.0000.9830.9780.9820.9881.0000.6811.0000.5400.8110.9710.5630.9781.0000.3820.786
키워드20.8710.9870.9831.0000.9950.9800.9490.0000.0001.0000.0000.9560.9580.7380.987NaN0.3380.797
키워드30.8060.9970.9780.9951.0000.9980.9751.0001.0000.0000.5340.9450.9590.7260.985NaN0.9470.821
키워드40.8470.9950.9820.9800.9981.0000.9781.0001.0000.0001.0000.9530.9691.0000.970NaN0.9530.498
소재지0.9430.9990.9880.9490.9750.9781.0000.0000.6810.8940.7620.9470.9390.8830.9910.0000.0000.772
용지면적0.0001.0001.0000.0001.0001.0000.0001.0001.0000.0000.0000.1060.0000.0000.0000.0000.0000.000
연면적0.1941.0000.6810.0001.0001.0000.6811.0001.0000.0000.0000.0000.0000.0110.000NaN0.0000.047
가격협의가능0.1031.0001.0001.0000.0000.0000.8940.0000.0001.0000.0000.0000.0790.2030.8250.0000.0000.000
공장등록여부0.4361.0000.5400.0000.5341.0000.7620.0000.0000.0001.0000.1220.3510.2210.6770.0000.0620.000
준공년도0.4450.9970.8110.9560.9450.9530.9470.1060.0000.0000.1221.0000.4790.2190.8390.0000.0000.000
용도지역0.0000.9990.9710.9580.9590.9690.9390.0000.0000.0790.3510.4791.0000.0000.0000.3240.5020.154
기계포함여부0.3810.8720.5630.7380.7261.0000.8830.0000.0110.2030.2210.2190.0001.0001.0000.0000.0700.000
게시기간0.9970.9950.9780.9870.9850.9700.9910.0000.0000.8250.6770.8390.0001.0001.0001.0000.0000.955
메인노출여부0.1411.0001.000NaNNaNNaN0.0000.000NaN0.0000.0000.0000.3240.0001.0001.0000.0480.000
진행상태0.0000.8620.3820.3380.9470.9530.0000.0000.0000.0000.0620.0000.5020.0700.0000.0481.0000.000
개인_기업구분0.3450.9760.7860.7970.8210.4980.7720.0000.0470.0000.0000.0000.1540.0000.9550.0000.0001.000
2023-12-13T08:30:19.764723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
메인노출여부진행상태개인_기업구분용도지역가격협의가능공장등록여부기계포함여부
메인노출여부1.0000.0780.0000.3080.0000.0000.000
진행상태0.0781.0000.0000.2440.0000.1000.113
개인_기업구분0.0000.0001.0000.1430.0000.0000.000
용도지역0.3080.2440.1431.0000.0680.3340.000
가격협의가능0.0000.0000.0000.0681.0000.0000.129
공장등록여부0.0000.1000.0000.3340.0001.0000.141
기계포함여부0.0000.1130.0000.0000.1290.1411.000
2023-12-13T08:30:19.884092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호용지면적연면적준공년도가격협의가능공장등록여부용도지역기계포함여부메인노출여부진행상태개인_기업구분
번호1.0000.0900.0520.1280.1010.4120.0000.3080.1350.0000.334
용지면적0.0901.0000.540-0.1100.0000.0000.0000.0000.0000.0000.000
연면적0.0520.5401.000-0.3910.0000.0000.0000.0001.0000.0000.073
준공년도0.128-0.110-0.3911.0000.0000.2490.2370.2450.0000.0410.000
가격협의가능0.1010.0000.0000.0001.0000.0000.0680.1290.0000.0000.000
공장등록여부0.4120.0000.0000.2490.0001.0000.3340.1410.0000.1000.000
용도지역0.0000.0000.0000.2370.0680.3341.0000.0000.3080.2440.143
기계포함여부0.3080.0000.0000.2450.1290.1410.0001.0000.0000.1130.000
메인노출여부0.1350.0001.0000.0000.0000.0000.3080.0001.0000.0780.000
진행상태0.0000.0000.0000.0410.0000.1000.2440.1130.0781.0000.000
개인_기업구분0.3340.0000.0730.0000.0000.0000.1430.0000.0000.0001.000

Missing values

2023-12-13T08:30:12.265182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:30:12.492116image/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.
2023-12-13T08:30:12.642230image/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

번호분류공장명키워드1키워드2키워드3키워드4소재지용지면적연면적가격협의가능공장등록여부준공년도용도지역기계포함여부게시기간메인노출여부진행상태개인_기업구분
010976공장예봉엔씨피일반공장의약품공장식품공장<NA>충청북도 음성군3316.06909.0가능Y2008일반공업지역N2015-12-14N등록개인
110965공장HIEO(주) 공장제조공장평택공장교통편리고덕신도시경기도 평택시4620.01172.0가능Y2008준도시지역<NA>2015-12-13N등록기업
210806공장(주)테크랜드공장임대<NA><NA>인천광역시 남구1782.02755.0가능Y1987일반공업지역<NA>2015-11-29N등록기업
310558공장해락원영어조합법인조미김 가공 공장<NA><NA><NA>충청남도 서천군3.1041.141가능Y2009기타<NA>2015-10-21N등록기업
410557공장테스트테스트<NA><NA><NA>서울특별시 양천구1234.01234.0가능Y2000농림지역Y2015-10-21N삭제기업
510536공장세미트로닉스청주공장<NA><NA>충청북도 청주시 흥덕구325.4324.48가능N2009준공업지역Y2015-08-31N등록기업
610366공장대호산업(주)공장매매공장부지토지<NA>충청남도 청양군8172.6<NA>가능Y2008공업지역Y2015-09-28N등록기업
710365공장대호산업(주)공장매매공장부지토지<NA>경상남도 밀양시9494.6<NA>가능Y2004공업지역<NA>2015-09-28N등록기업
810273공장(주)원림중부공장충북공장충주공장공장일반공장충청북도 충주시12121.05579.0가능Y2008관리지역Y2015-09-15N삭제기업
910272공장(주)원림중부공장충북공장충주공장공장일반공장충청북도 충주시12121.05579.37가능Y2008관리지역<NA>2015-09-15N등록기업
번호분류공장명키워드1키워드2키워드3키워드4소재지용지면적연면적가격협의가능공장등록여부준공년도용도지역기계포함여부게시기간메인노출여부진행상태개인_기업구분
741339공장식품제조 및 냉동창고, 사무실 및 숙소용 건물(일반제조공장으로 전환가능)식품제조공장공장창고충청남도 연기군495.0572.0가능N2012준도시지역Y2013-09-18N등록개인
751205공장경기 화성 팔탄 건평 100평팔탄면노하리<NA><NA>경기도 화성시330.0992.0가능N2006일반공업지역N2013-09-17N등록개인
76338공장(주)삼오 정읍공장매매공장부지매각공업용지전라북도 정읍시9490.87255.59가능Y1995공업지역N2013-08-18N등록기업
77337공장메타폴라아로마(주)오산평택<NA><NA>경기도 평택시3887.41500.0가능Y1995공업지역N2013-08-17N등록기업
78311공장씨에스텍제조물류<NA><NA>충청북도 진천군23000.0<NA>가능Y2009관리지역N2013-08-01N삭제기업
79259공장중앙인더스피아 투아파트형공장전용면적54.85평사무실&연구소창고경기도 성남시 중원구292.74<NA>가능N2002공업지역Y2013-12-31N등록기업
80257공장해성쏠라(주) 충주공장공장산업단지부동산공업지역충청북도 충주시3504.0825.0가능Y2007일반공업지역<NA>2013-06-30N삭제기업
81250공장화성정남I.C앞 800평공장공장고속도로인접경기화성깨끗한공장경기도 화성시2640.01221.0가능Y2001관리지역N2013-07-31N등록기업
82151공장식품가공공장 매매식품계란가공공장임대경기도 안성시4269.17354.88가능Y2000일반공업지역N2013-07-20N등록개인
83142공장의류 기계 생산의류의류기계<NA><NA>서울특별시 서대문구3300.03300.0가능Y2001공업지역Y2013-07-17N삭제기업