Overview

Dataset statistics

Number of variables12
Number of observations280
Missing cells536
Missing cells (%)16.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 KiB
Average record size in memory98.5 B

Variable types

Numeric2
Categorical4
Text2
DateTime2
Boolean2

Dataset

Description환경경영정보포털 사이트에서 관리하는 테이블 코드 정보 제공(그룹코드, 코드, 코드명, 코드설명, 등록자, 수정자, 삭제여부 등)
Author환경부
URLhttps://www.data.go.kr/data/15039236/fileData.do

Alerts

수정일 has constant value ""Constant
삭제여부 has constant value ""Constant
노출여부 has constant value ""Constant
수정자 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
코드설명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
그룹코드 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
등록자 is highly overall correlated with 수정자High correlation
연번 is highly overall correlated with 그룹코드 and 2 other fieldsHigh correlation
순서 is highly overall correlated with 수정자High correlation
등록자 is highly imbalanced (93.9%)Imbalance
수정자 is highly imbalanced (56.6%)Imbalance
등록일 has 255 (91.1%) missing valuesMissing
수정일 has 279 (99.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:48:59.644776
Analysis finished2023-12-12 11:49:01.253281
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct280
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.39286
Minimum1
Maximum415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T20:49:01.339746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.95
Q170.75
median140.5
Q3214.25
95-th percentile401.05
Maximum415
Range414
Interquartile range (IQR)143.5

Descriptive statistics

Standard deviation109.91552
Coefficient of variation (CV)0.6983514
Kurtosis-0.15503376
Mean157.39286
Median Absolute Deviation (MAD)72
Skewness0.75871227
Sum44070
Variance12081.422
MonotonicityNot monotonic
2023-12-12T20:49:01.488958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 1
 
0.4%
408 1
 
0.4%
414 1
 
0.4%
413 1
 
0.4%
412 1
 
0.4%
411 1
 
0.4%
410 1
 
0.4%
409 1
 
0.4%
407 1
 
0.4%
344 1
 
0.4%
Other values (270) 270
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
415 1
0.4%
414 1
0.4%
413 1
0.4%
412 1
0.4%
411 1
0.4%
410 1
0.4%
409 1
0.4%
408 1
0.4%
407 1
0.4%
406 1
0.4%

그룹코드
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
CS_CMP_CATE
25 
IDEA_KWD
23 
PRD_TYPE
22 
CS_CATE
20 
CS_OFF_AREA
19 
Other values (19)
171 

Length

Max length11
Median length9
Mean length8.0178571
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
CS_CMP_CATE 25
 
8.9%
IDEA_KWD 23
 
8.2%
PRD_TYPE 22
 
7.9%
CS_CATE 20
 
7.1%
CS_OFF_AREA 19
 
6.8%
G0004 18
 
6.4%
PH_ZONE 17
 
6.1%
EMAIL 16
 
5.7%
CM_BD 15
 
5.4%
SP_CATE 13
 
4.6%
Other values (14) 92
32.9%

Length

2023-12-12T20:49:01.636212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cs_cmp_cate 25
 
8.9%
idea_kwd 23
 
8.2%
prd_type 22
 
7.9%
cs_cate 20
 
7.1%
cs_off_area 19
 
6.8%
g0004 18
 
6.4%
ph_zone 17
 
6.1%
email 16
 
5.7%
cm_bd 15
 
5.4%
sp_cate 13
 
4.6%
Other values (14) 92
32.9%

코드
Text

Distinct267
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T20:49:01.989058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.3392857
Min length1

Characters and Unicode

Total characters1215
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique259 ?
Unique (%)92.5%

Sample

1st rowAR04
2nd rowAR05
3rd rowAR06
4th rowAR07
5th rowAR08
ValueCountFrequency (%)
5 3
 
1.1%
2 3
 
1.1%
1 3
 
1.1%
4 3
 
1.1%
3 3
 
1.1%
ds_biz 2
 
0.7%
6 2
 
0.7%
7 2
 
0.7%
18 1
 
0.4%
hs11 1
 
0.4%
Other values (257) 257
91.8%
2023-12-12T20:49:02.512227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 152
 
12.5%
1 121
 
10.0%
S 80
 
6.6%
P 75
 
6.2%
C 56
 
4.6%
R 51
 
4.2%
E 50
 
4.1%
D 49
 
4.0%
2 48
 
4.0%
I 47
 
3.9%
Other values (25) 486
40.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 713
58.7%
Decimal Number 500
41.2%
Connector Punctuation 2
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 80
11.2%
P 75
10.5%
C 56
 
7.9%
R 51
 
7.2%
E 50
 
7.0%
D 49
 
6.9%
I 47
 
6.6%
H 45
 
6.3%
A 42
 
5.9%
M 35
 
4.9%
Other values (14) 183
25.7%
Decimal Number
ValueCountFrequency (%)
0 152
30.4%
1 121
24.2%
2 48
 
9.6%
3 33
 
6.6%
4 29
 
5.8%
5 26
 
5.2%
9 25
 
5.0%
6 25
 
5.0%
7 21
 
4.2%
8 20
 
4.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 713
58.7%
Common 502
41.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 80
11.2%
P 75
10.5%
C 56
 
7.9%
R 51
 
7.2%
E 50
 
7.0%
D 49
 
6.9%
I 47
 
6.6%
H 45
 
6.3%
A 42
 
5.9%
M 35
 
4.9%
Other values (14) 183
25.7%
Common
ValueCountFrequency (%)
0 152
30.3%
1 121
24.1%
2 48
 
9.6%
3 33
 
6.6%
4 29
 
5.8%
5 26
 
5.2%
9 25
 
5.0%
6 25
 
5.0%
7 21
 
4.2%
8 20
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 152
 
12.5%
1 121
 
10.0%
S 80
 
6.6%
P 75
 
6.2%
C 56
 
4.6%
R 51
 
4.2%
E 50
 
4.1%
D 49
 
4.0%
2 48
 
4.0%
I 47
 
3.9%
Other values (25) 486
40.0%

코드명
Text

Distinct253
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T20:49:02.878079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length5.9392857
Min length1

Characters and Unicode

Total characters1663
Distinct characters247
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238 ?
Unique (%)85.0%

Sample

1st row충남/대전
2nd row경북/대구
3rd row전남/광주
4th row전북
5th row충북
ValueCountFrequency (%)
기타 10
 
2.8%
및 8
 
2.2%
개선 8
 
2.2%
에너지 7
 
1.9%
향상 7
 
1.9%
저감 5
 
1.4%
환경경영 5
 
1.4%
재활용 4
 
1.1%
효율 4
 
1.1%
녹색제품 3
 
0.8%
Other values (270) 298
83.0%
2023-12-12T20:49:03.408100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
 
4.8%
기 68
 
4.1%
경 68
 
4.1%
환 51
 
3.1%
/ 46
 
2.8%
지 37
 
2.2%
개 30
 
1.8%
술 26
 
1.6%
용 24
 
1.4%
업 24
 
1.4%
Other values (237) 1210
72.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1309
78.7%
Lowercase Letter 147
 
8.8%
Space Separator 79
 
4.8%
Other Punctuation 68
 
4.1%
Decimal Number 49
 
2.9%
Uppercase Letter 5
 
0.3%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 68
 
5.2%
경 68
 
5.2%
환 51
 
3.9%
지 37
 
2.8%
개 30
 
2.3%
술 26
 
2.0%
용 24
 
1.8%
업 24
 
1.8%
원 23
 
1.8%
자 23
 
1.8%
Other values (196) 935
71.4%
Lowercase Letter
ValueCountFrequency (%)
o 21
14.3%
m 19
12.9%
c 16
10.9%
a 16
10.9%
e 11
7.5%
r 10
 
6.8%
n 9
 
6.1%
i 8
 
5.4%
h 6
 
4.1%
l 6
 
4.1%
Other values (11) 25
17.0%
Decimal Number
ValueCountFrequency (%)
1 12
24.5%
3 7
14.3%
4 7
14.3%
2 6
12.2%
5 6
12.2%
6 5
10.2%
8 2
 
4.1%
0 2
 
4.1%
9 1
 
2.0%
7 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 46
67.6%
. 18
 
26.5%
, 2
 
2.9%
& 2
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
D 2
40.0%
R 2
40.0%
S 1
20.0%
Space Separator
ValueCountFrequency (%)
79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1309
78.7%
Common 202
 
12.1%
Latin 152
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 68
 
5.2%
경 68
 
5.2%
환 51
 
3.9%
지 37
 
2.8%
개 30
 
2.3%
술 26
 
2.0%
용 24
 
1.8%
업 24
 
1.8%
원 23
 
1.8%
자 23
 
1.8%
Other values (196) 935
71.4%
Latin
ValueCountFrequency (%)
o 21
13.8%
m 19
12.5%
c 16
10.5%
a 16
10.5%
e 11
 
7.2%
r 10
 
6.6%
n 9
 
5.9%
i 8
 
5.3%
h 6
 
3.9%
l 6
 
3.9%
Other values (14) 30
19.7%
Common
ValueCountFrequency (%)
79
39.1%
/ 46
22.8%
. 18
 
8.9%
1 12
 
5.9%
3 7
 
3.5%
4 7
 
3.5%
2 6
 
3.0%
5 6
 
3.0%
6 5
 
2.5%
( 3
 
1.5%
Other values (7) 13
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1309
78.7%
ASCII 354
 
21.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
22.3%
/ 46
13.0%
o 21
 
5.9%
m 19
 
5.4%
. 18
 
5.1%
c 16
 
4.5%
a 16
 
4.5%
1 12
 
3.4%
e 11
 
3.1%
r 10
 
2.8%
Other values (31) 106
29.9%
Hangul
ValueCountFrequency (%)
기 68
 
5.2%
경 68
 
5.2%
환 51
 
3.9%
지 37
 
2.8%
개 30
 
2.3%
술 26
 
2.0%
용 24
 
1.8%
업 24
 
1.8%
원 23
 
1.8%
자 23
 
1.8%
Other values (196) 935
71.4%

코드설명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
컨설팅 기업 분야
25 
에코디자인 아이디어 키워드
23 
에코디자인 제품 분류
22 
컨설턴트 분야
20 
오프라인 컨설팅 희망 지역
19 
Other values (26)
171 

Length

Max length15
Median length13
Mean length9.3571429
Min length2

Unique

Unique5 ?
Unique (%)1.8%

Sample

1st row컨설팅 기업 지역
2nd row컨설팅 기업 지역
3rd row컨설팅 기업 지역
4th row컨설팅 기업 지역
5th row컨설팅 기업 지역

Common Values

ValueCountFrequency (%)
컨설팅 기업 분야 25
 
8.9%
에코디자인 아이디어 키워드 23
 
8.2%
에코디자인 제품 분류 22
 
7.9%
컨설턴트 분야 20
 
7.1%
오프라인 컨설팅 희망 지역 19
 
6.8%
자가진단 체크리스트 항목 18
 
6.4%
유선전화 지역번호 17
 
6.1%
이메일 16
 
5.7%
전문인력 분야 13
 
4.6%
컨설팅 기업 지역 11
 
3.9%
Other values (21) 96
34.3%

Length

2023-12-12T20:49:03.576735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
컨설팅 65
 
9.7%
분야 65
 
9.7%
에코디자인 58
 
8.7%
기업 54
 
8.1%
지역 30
 
4.5%
키워드 23
 
3.4%
아이디어 23
 
3.4%
분류 22
 
3.3%
제품 22
 
3.3%
컨설턴트 20
 
3.0%
Other values (35) 285
42.7%

순서
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6071429
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T20:49:03.725731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q312
95-th percentile19.05
Maximum99
Range98
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.903265
Coefficient of variation (CV)0.91822166
Kurtosis60.322467
Mean8.6071429
Median Absolute Deviation (MAD)4
Skewness5.5954016
Sum2410
Variance62.461598
MonotonicityNot monotonic
2023-12-12T20:49:03.897599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 24
 
8.6%
2 24
 
8.6%
3 24
 
8.6%
4 21
 
7.5%
5 19
 
6.8%
6 18
 
6.4%
8 16
 
5.7%
7 15
 
5.4%
9 15
 
5.4%
10 14
 
5.0%
Other values (15) 90
32.1%
ValueCountFrequency (%)
1 24
8.6%
2 24
8.6%
3 24
8.6%
4 21
7.5%
5 19
6.8%
6 18
6.4%
7 15
5.4%
8 16
5.7%
9 15
5.4%
10 14
5.0%
ValueCountFrequency (%)
99 1
 
0.4%
24 1
 
0.4%
23 2
 
0.7%
22 3
 
1.1%
21 3
 
1.1%
20 4
1.4%
19 5
1.8%
18 6
2.1%
17 7
2.5%
16 8
2.9%

등록자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
admin
278 
SYSTEM
 
2

Length

Max length6
Median length5
Mean length5.0071429
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
admin 278
99.3%
SYSTEM 2
 
0.7%

Length

2023-12-12T20:49:04.091604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:49:04.245160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
admin 278
99.3%
system 2
 
0.7%

등록일
Date

MISSING 

Distinct3
Distinct (%)12.0%
Missing255
Missing (%)91.1%
Memory size2.3 KiB
Minimum2017-11-03 00:00:00
Maximum2017-12-15 00:00:00
2023-12-12T20:49:04.365901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:49:04.504516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

수정자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
admin
255 
<NA>
 
25

Length

Max length5
Median length5
Mean length4.9107143
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
admin 255
91.1%
<NA> 25
 
8.9%

Length

2023-12-12T20:49:04.655513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:49:04.789623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
admin 255
91.1%
na 25
 
8.9%

수정일
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing279
Missing (%)99.6%
Memory size2.3 KiB
Minimum2017-12-15 00:00:00
Maximum2017-12-15 00:00:00
2023-12-12T20:49:04.908064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:49:05.041770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
False
280 
ValueCountFrequency (%)
False 280
100.0%
2023-12-12T20:49:05.153436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

노출여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing2
Missing (%)0.7%
Memory size692.0 B
True
278 
(Missing)
 
2
ValueCountFrequency (%)
True 278
99.3%
(Missing) 2
 
0.7%
2023-12-12T20:49:05.240809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T20:49:00.366307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:49:00.181741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:49:00.465675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:49:00.258173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:49:05.306660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번그룹코드코드설명순서등록자등록일
연번1.0000.9960.9830.3180.4121.000
그룹코드0.9961.0001.0000.3850.0001.000
코드설명0.9831.0001.0000.2930.0001.000
순서0.3180.3850.2931.0000.6520.953
등록자0.4120.0000.0000.6521.0001.000
등록일1.0001.0001.0000.9531.0001.000
2023-12-12T20:49:05.426929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수정자코드설명그룹코드등록자
수정자1.0001.0001.0001.000
코드설명1.0001.0000.9840.000
그룹코드1.0000.9841.0000.000
등록자1.0000.0000.0001.000
2023-12-12T20:49:05.568396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번순서그룹코드코드설명등록자수정자
연번1.000-0.1740.8690.8580.3071.000
순서-0.1741.0000.1830.1460.4551.000
그룹코드0.8690.1831.0000.9840.0001.000
코드설명0.8580.1460.9841.0000.0001.000
등록자0.3070.4550.0000.0001.0001.000
수정자1.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T20:49:00.585627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:49:00.772530image/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.
2023-12-12T20:49:01.180077image/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

연번그룹코드코드코드명코드설명순서등록자등록일수정자수정일삭제여부노출여부
070CS_CMP_AREAAR04충남/대전컨설팅 기업 지역4admin<NA>admin<NA>NY
171CS_CMP_AREAAR05경북/대구컨설팅 기업 지역5admin<NA>admin<NA>NY
272CS_CMP_AREAAR06전남/광주컨설팅 기업 지역6admin<NA>admin<NA>NY
373CS_CMP_AREAAR07전북컨설팅 기업 지역7admin<NA>admin<NA>NY
474CS_CMP_AREAAR08충북컨설팅 기업 지역8admin<NA>admin<NA>NY
575CS_CMP_AREAAR09강원컨설팅 기업 지역9admin<NA>admin<NA>NY
676CS_CMP_AREAAR10제주컨설팅 기업 지역10admin<NA>admin<NA>NY
777CS_CMP_AREAAR11해외컨설팅 기업 지역11admin<NA>admin<NA>NY
878CS_OFF_AREAHR01지역환경기술개발센터연합회오프라인 컨설팅 희망 지역1admin<NA>admin<NA>NY
979CS_OFF_AREAHR02인천지역환경기술개발센터오프라인 컨설팅 희망 지역2admin<NA>admin<NA>NY
연번그룹코드코드코드명코드설명순서등록자등록일수정자수정일삭제여부노출여부
27060CS_BIZ_GRPBG04측정분석장치제조업컨설팅 기업 업종4admin<NA>admin<NA>NY
27161CS_BIZ_GRPBG05에너지컨설팅 기업 업종5admin<NA>admin<NA>NY
27262CS_BIZ_GRPBG06복원/재활용업컨설팅 기업 업종6admin<NA>admin<NA>NY
27363CS_BIZ_GRPBG07서비스/대행업컨설팅 기업 업종7admin<NA>admin<NA>NY
27464CS_BIZ_GRPBG08경영컨설팅컨설팅 기업 업종8admin<NA>admin<NA>NY
27565CS_BIZ_GRPBG09제품/소재제조업컨설팅 기업 업종9admin<NA>admin<NA>NY
27666CS_BIZ_GRPBG10기타컨설팅 기업 업종10admin<NA>admin<NA>NY
27767CS_CMP_AREAAR01서울컨설팅 기업 지역1admin<NA>admin<NA>NY
27868CS_CMP_AREAAR02경기/인천컨설팅 기업 지역2admin<NA>admin<NA>NY
27969CS_CMP_AREAAR03경남/부산컨설팅 기업 지역3admin<NA>admin<NA>NY