Overview

Dataset statistics

Number of variables19
Number of observations8131
Missing cells16709
Missing cells (%)10.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory159.0 B

Variable types

Numeric7
Categorical5
Text7

Dataset

Description대구광역시 계획입지 공장등록 현황(팩토리온 검색 현황), 공장 연락처는 포함되어 제공하지 않음을 양지하여 주시기 바랍니다.
URLhttps://www.data.go.kr/data/15073542/fileData.do

Alerts

지식산업센터명 is highly overall correlated with 남종업원 and 7 other fieldsHigh correlation
단지명 is highly overall correlated with 지식산업센터명 and 2 other fieldsHigh correlation
관할조직명 is highly overall correlated with 단지명 and 2 other fieldsHigh correlation
대표업종번호 is highly overall correlated with 사업유형High correlation
남종업원 is highly overall correlated with 여종업원 and 2 other fieldsHigh correlation
여종업원 is highly overall correlated with 남종업원 and 2 other fieldsHigh correlation
외국인(남) is highly overall correlated with 외국인(여) and 1 other fieldsHigh correlation
외국인(여) is highly overall correlated with 외국인(남)High correlation
종업원수 is highly overall correlated with 남종업원 and 2 other fieldsHigh correlation
설립구분 is highly overall correlated with 단지명 and 2 other fieldsHigh correlation
사업유형 is highly overall correlated with 대표업종번호 and 1 other fieldsHigh correlation
지식산업센터명 is highly imbalanced (81.4%)Imbalance
설립구분 is highly imbalanced (82.8%)Imbalance
공장대표주소(도로명) has 137 (1.7%) missing valuesMissing
남종업원 has 799 (9.8%) missing valuesMissing
여종업원 has 1820 (22.4%) missing valuesMissing
외국인(남) has 5093 (62.6%) missing valuesMissing
외국인(여) has 5245 (64.5%) missing valuesMissing
주원자재 has 3542 (43.6%) missing valuesMissing
남종업원 is highly skewed (γ1 = 61.11016994)Skewed
여종업원 is highly skewed (γ1 = 31.53498767)Skewed
외국인(남) is highly skewed (γ1 = 40.91659144)Skewed
종업원수 is highly skewed (γ1 = 55.03637369)Skewed
순번 has unique valuesUnique
남종업원 has 211 (2.6%) zerosZeros
여종업원 has 751 (9.2%) zerosZeros
외국인(남) has 2626 (32.3%) zerosZeros
외국인(여) has 2746 (33.8%) zerosZeros
종업원수 has 888 (10.9%) zerosZeros

Reproduction

Analysis started2023-12-12 19:51:01.601100
Analysis finished2023-12-12 19:51:13.229339
Duration11.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct8131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4066
Minimum1
Maximum8131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.6 KiB
2023-12-13T04:51:13.320269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile407.5
Q12033.5
median4066
Q36098.5
95-th percentile7724.5
Maximum8131
Range8130
Interquartile range (IQR)4065

Descriptive statistics

Standard deviation2347.3619
Coefficient of variation (CV)0.57731477
Kurtosis-1.2
Mean4066
Median Absolute Deviation (MAD)2033
Skewness0
Sum33060646
Variance5510107.7
MonotonicityStrictly increasing
2023-12-13T04:51:13.507132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5418 1
 
< 0.1%
5431 1
 
< 0.1%
5430 1
 
< 0.1%
5429 1
 
< 0.1%
5428 1
 
< 0.1%
5427 1
 
< 0.1%
5426 1
 
< 0.1%
5425 1
 
< 0.1%
5424 1
 
< 0.1%
Other values (8121) 8121
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8131 1
< 0.1%
8130 1
< 0.1%
8129 1
< 0.1%
8128 1
< 0.1%
8127 1
< 0.1%
8126 1
< 0.1%
8125 1
< 0.1%
8124 1
< 0.1%
8123 1
< 0.1%
8122 1
< 0.1%

단지명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
성서지방산업단지
3157 
서대구일반산업단지
1456 
대구제3일반산업단지
996 
북구검단지방산업단지
599 
달성일반산업단지
383 
Other values (16)
1540 

Length

Max length20
Median length8
Mean length8.9419506
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성서지방산업단지
2nd row서대구일반산업단지
3rd row대구제3일반산업단지
4th row달성2차일반산업단지
5th row성서5차첨단산업단지

Common Values

ValueCountFrequency (%)
성서지방산업단지 3157
38.8%
서대구일반산업단지 1456
17.9%
대구제3일반산업단지 996
 
12.2%
북구검단지방산업단지 599
 
7.4%
달성일반산업단지 383
 
4.7%
달성2차일반산업단지 284
 
3.5%
대구국가산업단지 261
 
3.2%
대구염색일반산업단지 157
 
1.9%
대구연구개발특구 127
 
1.6%
대구테크노폴리스일반산업단지 124
 
1.5%
Other values (11) 587
 
7.2%

Length

2023-12-13T04:51:13.702676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성서지방산업단지 3157
38.8%
서대구일반산업단지 1456
17.9%
대구제3일반산업단지 996
 
12.2%
북구검단지방산업단지 599
 
7.4%
달성일반산업단지 383
 
4.7%
달성2차일반산업단지 284
 
3.5%
대구국가산업단지 261
 
3.2%
대구염색일반산업단지 157
 
1.9%
대구연구개발특구 127
 
1.6%
대구테크노폴리스일반산업단지 124
 
1.5%
Other values (11) 587
 
7.2%
Distinct7263
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
2023-12-13T04:51:14.033947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length23
Mean length6.5875046
Min length1

Characters and Unicode

Total characters53563
Distinct characters703
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6575 ?
Unique (%)80.9%

Sample

1st row( 주)명장
2nd row(SM)컨설팅
3rd row(사)한국장애인문화협회
4th row(사)한국지체장애인협회 달성군장애인재활자립작업장
5th row(유)대구특수금속
ValueCountFrequency (%)
주식회사 253
 
2.9%
제2공장 34
 
0.4%
기숙사 17
 
0.2%
개인 17
 
0.2%
대구지점 16
 
0.2%
2공장 16
 
0.2%
대구공장 11
 
0.1%
8
 
0.1%
제1공장 8
 
0.1%
지점 6
 
0.1%
Other values (7343) 8420
95.6%
2023-12-13T04:51:14.822877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4002
 
7.5%
) 3876
 
7.2%
( 3875
 
7.2%
1642
 
3.1%
1348
 
2.5%
1033
 
1.9%
923
 
1.7%
871
 
1.6%
862
 
1.6%
847
 
1.6%
Other values (693) 34284
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43469
81.2%
Close Punctuation 3876
 
7.2%
Open Punctuation 3875
 
7.2%
Uppercase Letter 1062
 
2.0%
Space Separator 751
 
1.4%
Decimal Number 301
 
0.6%
Other Punctuation 106
 
0.2%
Lowercase Letter 98
 
0.2%
Dash Punctuation 23
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4002
 
9.2%
1642
 
3.8%
1348
 
3.1%
1033
 
2.4%
923
 
2.1%
871
 
2.0%
862
 
2.0%
847
 
1.9%
795
 
1.8%
768
 
1.8%
Other values (628) 30378
69.9%
Uppercase Letter
ValueCountFrequency (%)
E 116
 
10.9%
T 88
 
8.3%
S 84
 
7.9%
C 76
 
7.2%
N 70
 
6.6%
D 63
 
5.9%
M 62
 
5.8%
G 53
 
5.0%
I 49
 
4.6%
A 48
 
4.5%
Other values (16) 353
33.2%
Lowercase Letter
ValueCountFrequency (%)
e 17
17.3%
o 11
11.2%
h 7
 
7.1%
a 7
 
7.1%
n 6
 
6.1%
i 6
 
6.1%
t 6
 
6.1%
c 6
 
6.1%
l 4
 
4.1%
r 4
 
4.1%
Other values (10) 24
24.5%
Decimal Number
ValueCountFrequency (%)
2 126
41.9%
1 62
20.6%
3 26
 
8.6%
9 17
 
5.6%
4 14
 
4.7%
0 14
 
4.7%
6 13
 
4.3%
5 13
 
4.3%
7 10
 
3.3%
8 6
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 61
57.5%
& 34
32.1%
, 9
 
8.5%
/ 2
 
1.9%
Close Punctuation
ValueCountFrequency (%)
) 3876
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3875
100.0%
Space Separator
ValueCountFrequency (%)
751
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43471
81.2%
Common 8932
 
16.7%
Latin 1160
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4002
 
9.2%
1642
 
3.8%
1348
 
3.1%
1033
 
2.4%
923
 
2.1%
871
 
2.0%
862
 
2.0%
847
 
1.9%
795
 
1.8%
768
 
1.8%
Other values (629) 30380
69.9%
Latin
ValueCountFrequency (%)
E 116
 
10.0%
T 88
 
7.6%
S 84
 
7.2%
C 76
 
6.6%
N 70
 
6.0%
D 63
 
5.4%
M 62
 
5.3%
G 53
 
4.6%
I 49
 
4.2%
A 48
 
4.1%
Other values (36) 451
38.9%
Common
ValueCountFrequency (%)
) 3876
43.4%
( 3875
43.4%
751
 
8.4%
2 126
 
1.4%
1 62
 
0.7%
. 61
 
0.7%
& 34
 
0.4%
3 26
 
0.3%
- 23
 
0.3%
9 17
 
0.2%
Other values (8) 81
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43469
81.2%
ASCII 10092
 
18.8%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4002
 
9.2%
1642
 
3.8%
1348
 
3.1%
1033
 
2.4%
923
 
2.1%
871
 
2.0%
862
 
2.0%
847
 
1.9%
795
 
1.8%
768
 
1.8%
Other values (628) 30378
69.9%
ASCII
ValueCountFrequency (%)
) 3876
38.4%
( 3875
38.4%
751
 
7.4%
2 126
 
1.2%
E 116
 
1.1%
T 88
 
0.9%
S 84
 
0.8%
C 76
 
0.8%
N 70
 
0.7%
D 63
 
0.6%
Other values (54) 967
 
9.6%
None
ValueCountFrequency (%)
2
100.0%

지식산업센터명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
<NA>
7319 
디센터 1976
 
219
이앤씨 벤처드림타워8
 
98
검단팩토리밸리
 
97
성서드림타운
 
64
Other values (16)
 
334

Length

Max length16
Median length4
Mean length4.4566474
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row디센터 1976
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 7319
90.0%
디센터 1976 219
 
2.7%
이앤씨 벤처드림타워8 98
 
1.2%
검단팩토리밸리 97
 
1.2%
성서드림타운 64
 
0.8%
MJ테크노파크 60
 
0.7%
일신테크노밸리 56
 
0.7%
서대구산단 복합지식산업센터 35
 
0.4%
비젼지식산업센터 30
 
0.4%
뉴비젼 지식산업센터 28
 
0.3%
Other values (11) 125
 
1.5%

Length

2023-12-13T04:51:14.976684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7319
85.7%
1976 219
 
2.6%
디센터 219
 
2.6%
이앤씨 98
 
1.1%
벤처드림타워8 98
 
1.1%
검단팩토리밸리 97
 
1.1%
성서드림타운 64
 
0.7%
mj테크노파크 60
 
0.7%
일신테크노밸리 56
 
0.7%
서대구산단 35
 
0.4%
Other values (18) 271
 
3.2%
Distinct6867
Distinct (%)85.9%
Missing137
Missing (%)1.7%
Memory size63.7 KiB
2023-12-13T04:51:15.285664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length105
Median length69
Mean length33.521016
Min length18

Characters and Unicode

Total characters267967
Distinct characters334
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

Unique5996 ?
Unique (%)75.0%

Sample

1st row대구광역시 달서구 호산동로7길 42, (3차단지 95B 1-7L) (호림동)
2nd row대구광역시 서구 와룡로 307, 1층 104호(중리동) 1층 104호
3rd row대구광역시 북구 노원로1길 160 (노원동3가)
4th row대구광역시 달성군 구지면 달성2차로 221
5th row대구광역시 달성군 다사읍 세천로3길 69, 성서5차첨단산업단지 6B 4L
ValueCountFrequency (%)
대구광역시 7996
 
15.2%
달서구 3265
 
6.2%
서구 1604
 
3.0%
북구 1593
 
3.0%
달성군 1259
 
2.4%
2차단지 1102
 
2.1%
1차단지 910
 
1.7%
노원동3가 905
 
1.7%
갈산동 751
 
1.4%
월암동 601
 
1.1%
Other values (4018) 32730
62.1%
2023-12-13T04:51:15.749615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44788
 
16.7%
15517
 
5.8%
1 11221
 
4.2%
9489
 
3.5%
8321
 
3.1%
) 8259
 
3.1%
( 8255
 
3.1%
8033
 
3.0%
8006
 
3.0%
8000
 
3.0%
Other values (324) 138078
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141311
52.7%
Decimal Number 51537
 
19.2%
Space Separator 44788
 
16.7%
Close Punctuation 8260
 
3.1%
Open Punctuation 8256
 
3.1%
Uppercase Letter 6416
 
2.4%
Other Punctuation 4528
 
1.7%
Dash Punctuation 2825
 
1.1%
Lowercase Letter 40
 
< 0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15517
 
11.0%
9489
 
6.7%
8321
 
5.9%
8033
 
5.7%
8006
 
5.7%
8000
 
5.7%
7882
 
5.6%
7594
 
5.4%
6233
 
4.4%
5858
 
4.1%
Other values (276) 56378
39.9%
Uppercase Letter
ValueCountFrequency (%)
B 3050
47.5%
L 2923
45.6%
A 214
 
3.3%
F 95
 
1.5%
R 29
 
0.5%
D 28
 
0.4%
M 17
 
0.3%
S 11
 
0.2%
J 10
 
0.2%
P 9
 
0.1%
Other values (11) 30
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 11221
21.8%
2 7585
14.7%
3 7409
14.4%
5 4479
 
8.7%
4 4191
 
8.1%
0 3981
 
7.7%
7 3761
 
7.3%
6 3470
 
6.7%
9 2852
 
5.5%
8 2588
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
l 9
22.5%
d 7
17.5%
r 7
17.5%
w 7
17.5%
o 7
17.5%
b 3
 
7.5%
Other Punctuation
ValueCountFrequency (%)
, 4420
97.6%
/ 78
 
1.7%
& 20
 
0.4%
. 10
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 8259
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 8255
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
44788
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2825
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141311
52.7%
Common 120200
44.9%
Latin 6456
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15517
 
11.0%
9489
 
6.7%
8321
 
5.9%
8033
 
5.7%
8006
 
5.7%
8000
 
5.7%
7882
 
5.6%
7594
 
5.4%
6233
 
4.4%
5858
 
4.1%
Other values (276) 56378
39.9%
Latin
ValueCountFrequency (%)
B 3050
47.2%
L 2923
45.3%
A 214
 
3.3%
F 95
 
1.5%
R 29
 
0.4%
D 28
 
0.4%
M 17
 
0.3%
S 11
 
0.2%
J 10
 
0.2%
l 9
 
0.1%
Other values (17) 70
 
1.1%
Common
ValueCountFrequency (%)
44788
37.3%
1 11221
 
9.3%
) 8259
 
6.9%
( 8255
 
6.9%
2 7585
 
6.3%
3 7409
 
6.2%
5 4479
 
3.7%
, 4420
 
3.7%
4 4191
 
3.5%
0 3981
 
3.3%
Other values (11) 15612
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141311
52.7%
ASCII 126656
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44788
35.4%
1 11221
 
8.9%
) 8259
 
6.5%
( 8255
 
6.5%
2 7585
 
6.0%
3 7409
 
5.8%
5 4479
 
3.5%
, 4420
 
3.5%
4 4191
 
3.3%
0 3981
 
3.1%
Other values (38) 22068
17.4%
Hangul
ValueCountFrequency (%)
15517
 
11.0%
9489
 
6.7%
8321
 
5.9%
8033
 
5.7%
8006
 
5.7%
8000
 
5.7%
7882
 
5.6%
7594
 
5.4%
6233
 
4.4%
5858
 
4.1%
Other values (276) 56378
39.9%
Distinct7085
Distinct (%)87.2%
Missing6
Missing (%)0.1%
Memory size63.7 KiB
2023-12-13T04:51:16.061898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length103
Median length70
Mean length27.234338
Min length11

Characters and Unicode

Total characters221279
Distinct characters296
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

Unique6298 ?
Unique (%)77.5%

Sample

1st row대구광역시 달서구 호림동 2-18번지
2nd row대구광역시 서구 중리동 1166-1 1층 104호 1층 104호
3rd row대구광역시 북구 노원동3가 39번지
4th row대구광역시 달성군 구지면 예현리 757-12 달성군장애인재활자립작업장
5th row대구광역시 달성군 다사읍 세천리 1670-4번지 성서5차첨단산업단지 6B 4L
ValueCountFrequency (%)
대구광역시 8117
 
18.4%
달서구 3270
 
7.4%
북구 1662
 
3.8%
서구 1607
 
3.6%
달성군 1301
 
2.9%
노원동3가 978
 
2.2%
갈산동 831
 
1.9%
중리동 736
 
1.7%
이현동 724
 
1.6%
검단동 666
 
1.5%
Other values (6799) 24289
55.0%
2023-12-13T04:51:16.535127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36526
 
16.5%
15621
 
7.1%
1 11829
 
5.3%
9014
 
4.1%
8685
 
3.9%
8120
 
3.7%
8120
 
3.7%
8118
 
3.7%
- 7802
 
3.5%
7534
 
3.4%
Other values (286) 99910
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118055
53.4%
Decimal Number 52050
23.5%
Space Separator 36526
 
16.5%
Dash Punctuation 7802
 
3.5%
Uppercase Letter 4359
 
2.0%
Close Punctuation 1080
 
0.5%
Open Punctuation 1077
 
0.5%
Other Punctuation 286
 
0.1%
Lowercase Letter 40
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15621
13.2%
9014
 
7.6%
8685
 
7.4%
8120
 
6.9%
8120
 
6.9%
8118
 
6.9%
7534
 
6.4%
6162
 
5.2%
5061
 
4.3%
4646
 
3.9%
Other values (240) 36974
31.3%
Uppercase Letter
ValueCountFrequency (%)
B 1995
45.8%
L 1862
42.7%
A 282
 
6.5%
F 95
 
2.2%
R 30
 
0.7%
D 25
 
0.6%
M 16
 
0.4%
C 8
 
0.2%
S 8
 
0.2%
J 8
 
0.2%
Other values (9) 30
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 11829
22.7%
2 6867
13.2%
3 6282
12.1%
0 5246
10.1%
4 4229
 
8.1%
6 3744
 
7.2%
8 3698
 
7.1%
5 3657
 
7.0%
7 3652
 
7.0%
9 2846
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
l 9
22.5%
d 7
17.5%
r 7
17.5%
w 7
17.5%
o 7
17.5%
b 3
 
7.5%
Other Punctuation
ValueCountFrequency (%)
, 229
80.1%
/ 32
 
11.2%
& 18
 
6.3%
. 7
 
2.4%
Close Punctuation
ValueCountFrequency (%)
) 1079
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1076
99.9%
[ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
36526
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7802
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118055
53.4%
Common 98825
44.7%
Latin 4399
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15621
13.2%
9014
 
7.6%
8685
 
7.4%
8120
 
6.9%
8120
 
6.9%
8118
 
6.9%
7534
 
6.4%
6162
 
5.2%
5061
 
4.3%
4646
 
3.9%
Other values (240) 36974
31.3%
Latin
ValueCountFrequency (%)
B 1995
45.4%
L 1862
42.3%
A 282
 
6.4%
F 95
 
2.2%
R 30
 
0.7%
D 25
 
0.6%
M 16
 
0.4%
l 9
 
0.2%
C 8
 
0.2%
S 8
 
0.2%
Other values (15) 69
 
1.6%
Common
ValueCountFrequency (%)
36526
37.0%
1 11829
 
12.0%
- 7802
 
7.9%
2 6867
 
6.9%
3 6282
 
6.4%
0 5246
 
5.3%
4 4229
 
4.3%
6 3744
 
3.8%
8 3698
 
3.7%
5 3657
 
3.7%
Other values (11) 8945
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118055
53.4%
ASCII 103224
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36526
35.4%
1 11829
 
11.5%
- 7802
 
7.6%
2 6867
 
6.7%
3 6282
 
6.1%
0 5246
 
5.1%
4 4229
 
4.1%
6 3744
 
3.6%
8 3698
 
3.6%
5 3657
 
3.5%
Other values (36) 13344
 
12.9%
Hangul
ValueCountFrequency (%)
15621
13.2%
9014
 
7.6%
8685
 
7.4%
8120
 
6.9%
8120
 
6.9%
8118
 
6.9%
7534
 
6.4%
6162
 
5.2%
5061
 
4.3%
4646
 
3.9%
Other values (240) 36974
31.3%

대표업종번호
Real number (ℝ)

HIGH CORRELATION 

Distinct566
Distinct (%)7.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30481.525
Minimum10112
Maximum96911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.6 KiB
2023-12-13T04:51:16.673325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10112
5-th percentile13213
Q122251
median27213
Q330320
95-th percentile68112
Maximum96911
Range86799
Interquartile range (IQR)8069

Descriptive statistics

Standard deviation16526.274
Coefficient of variation (CV)0.54217347
Kurtosis2.5927612
Mean30481.525
Median Absolute Deviation (MAD)3118
Skewness1.7153572
Sum2.4778432 × 108
Variance2.7311775 × 108
MonotonicityNot monotonic
2023-12-13T04:51:16.806603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68112 494
 
6.1%
30399 410
 
5.0%
25924 329
 
4.0%
29294 287
 
3.5%
13213 206
 
2.5%
28123 203
 
2.5%
25929 165
 
2.0%
13402 147
 
1.8%
25922 145
 
1.8%
29299 124
 
1.5%
Other values (556) 5619
69.1%
ValueCountFrequency (%)
10112 1
 
< 0.1%
10121 13
0.2%
10122 3
 
< 0.1%
10129 19
0.2%
10211 4
 
< 0.1%
10212 1
 
< 0.1%
10213 2
 
< 0.1%
10219 1
 
< 0.1%
10301 4
 
< 0.1%
10302 1
 
< 0.1%
ValueCountFrequency (%)
96911 3
 
< 0.1%
95213 8
 
0.1%
95212 12
 
0.1%
95211 43
0.5%
91139 1
 
< 0.1%
91136 2
 
< 0.1%
91135 1
 
< 0.1%
91111 1
 
< 0.1%
90211 1
 
< 0.1%
87210 1
 
< 0.1%
Distinct1963
Distinct (%)24.1%
Missing2
Missing (%)< 0.1%
Memory size63.7 KiB
2023-12-13T04:51:17.116699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length299
Median length5
Mean length9.480994
Min length5

Characters and Unicode

Total characters77071
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

Unique1415 ?
Unique (%)17.4%

Sample

1st row30399+29294+30391+30392+30400
2nd row68112
3rd row17901+18111+18113+18119
4th row16292+18119+22231+32029+33933
5th row29294
ValueCountFrequency (%)
68112 482
 
5.9%
25924 292
 
3.6%
30399+30391+30392+30400 260
 
3.2%
29294 246
 
3.0%
13213 178
 
2.2%
28123 153
 
1.9%
25929 146
 
1.8%
25922 140
 
1.7%
13402 133
 
1.6%
30399 88
 
1.1%
Other values (1953) 6011
73.9%
2023-12-13T04:51:17.675733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 18506
24.0%
1 13203
17.1%
9 11145
14.5%
3 8904
11.6%
0 6168
 
8.0%
+ 6071
 
7.9%
4 4138
 
5.4%
5 3191
 
4.1%
8 2116
 
2.7%
6 1928
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71000
92.1%
Math Symbol 6071
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18506
26.1%
1 13203
18.6%
9 11145
15.7%
3 8904
12.5%
0 6168
 
8.7%
4 4138
 
5.8%
5 3191
 
4.5%
8 2116
 
3.0%
6 1928
 
2.7%
7 1701
 
2.4%
Math Symbol
ValueCountFrequency (%)
+ 6071
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77071
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18506
24.0%
1 13203
17.1%
9 11145
14.5%
3 8904
11.6%
0 6168
 
8.0%
+ 6071
 
7.9%
4 4138
 
5.4%
5 3191
 
4.1%
8 2116
 
2.7%
6 1928
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18506
24.0%
1 13203
17.1%
9 11145
14.5%
3 8904
11.6%
0 6168
 
8.0%
+ 6071
 
7.9%
4 4138
 
5.4%
5 3191
 
4.1%
8 2116
 
2.7%
6 1928
 
2.5%
Distinct1310
Distinct (%)16.1%
Missing2
Missing (%)< 0.1%
Memory size63.7 KiB
2023-12-13T04:51:18.066821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length15.955714
Min length3

Characters and Unicode

Total characters129704
Distinct characters378
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique628 ?
Unique (%)7.7%

Sample

1st row그 외 자동차용 신품 부품 제조업 외 4 종
2nd row비주거용 건물 임대업
3rd row문구용 종이제품 제조업 외 3 종
4th row장식용 목제품 제조업 외 4 종
5th row주형 및 금형 제조업
ValueCountFrequency (%)
제조업 5182
 
12.5%
3918
 
9.4%
3302
 
7.9%
2667
 
6.4%
기타 1779
 
4.3%
1 1475
 
3.5%
1250
 
3.0%
신품 692
 
1.7%
자동차용 600
 
1.4%
부품 597
 
1.4%
Other values (921) 20118
48.4%
2023-12-13T04:51:18.662767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33454
25.8%
8551
 
6.6%
6773
 
5.2%
6188
 
4.8%
3961
 
3.1%
3910
 
3.0%
3311
 
2.6%
2780
 
2.1%
2582
 
2.0%
2575
 
2.0%
Other values (368) 55619
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92600
71.4%
Space Separator 33454
 
25.8%
Decimal Number 2808
 
2.2%
Other Punctuation 798
 
0.6%
Close Punctuation 22
 
< 0.1%
Open Punctuation 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8551
 
9.2%
6773
 
7.3%
6188
 
6.7%
3961
 
4.3%
3910
 
4.2%
3311
 
3.6%
2780
 
3.0%
2582
 
2.8%
2575
 
2.8%
1806
 
2.0%
Other values (353) 50163
54.2%
Decimal Number
ValueCountFrequency (%)
1 1612
57.4%
3 430
 
15.3%
2 423
 
15.1%
4 132
 
4.7%
5 78
 
2.8%
6 55
 
2.0%
7 29
 
1.0%
8 24
 
0.9%
9 16
 
0.6%
0 9
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 781
97.9%
. 17
 
2.1%
Space Separator
ValueCountFrequency (%)
33454
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92600
71.4%
Common 37104
28.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8551
 
9.2%
6773
 
7.3%
6188
 
6.7%
3961
 
4.3%
3910
 
4.2%
3311
 
3.6%
2780
 
3.0%
2582
 
2.8%
2575
 
2.8%
1806
 
2.0%
Other values (353) 50163
54.2%
Common
ValueCountFrequency (%)
33454
90.2%
1 1612
 
4.3%
, 781
 
2.1%
3 430
 
1.2%
2 423
 
1.1%
4 132
 
0.4%
5 78
 
0.2%
6 55
 
0.1%
7 29
 
0.1%
8 24
 
0.1%
Other values (5) 86
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92548
71.4%
ASCII 37104
28.6%
Compat Jamo 52
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33454
90.2%
1 1612
 
4.3%
, 781
 
2.1%
3 430
 
1.2%
2 423
 
1.1%
4 132
 
0.4%
5 78
 
0.2%
6 55
 
0.1%
7 29
 
0.1%
8 24
 
0.1%
Other values (5) 86
 
0.2%
Hangul
ValueCountFrequency (%)
8551
 
9.2%
6773
 
7.3%
6188
 
6.7%
3961
 
4.3%
3910
 
4.2%
3311
 
3.6%
2780
 
3.0%
2582
 
2.8%
2575
 
2.8%
1806
 
2.0%
Other values (352) 50111
54.1%
Compat Jamo
ValueCountFrequency (%)
52
100.0%

관할조직명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
대구성서산업단지관리공단
3391 
서대구산업단지관리공단
1456 
대구제3산업단지관리공단
995 
(사)대구검단산업단지관리공단
726 
한국산업단지공단 대구지역본부 달성사무소
552 
Other values (11)
1011 

Length

Max length30
Median length12
Mean length12.974419
Min length7

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row대구성서산업단지관리공단
2nd row서대구산업단지관리공단
3rd row대구제3산업단지관리공단
4th row한국산업단지공단 대구지역본부 달성사무소
5th row대구성서산업단지관리공단

Common Values

ValueCountFrequency (%)
대구성서산업단지관리공단 3391
41.7%
서대구산업단지관리공단 1456
17.9%
대구제3산업단지관리공단 995
 
12.2%
(사)대구검단산업단지관리공단 726
 
8.9%
한국산업단지공단 대구지역본부 달성사무소 552
 
6.8%
달성1차산업단지관리공단 383
 
4.7%
연구개발특구지원본부 대구기술사업화센터 운영지원팀 196
 
2.4%
대구염색산업단지관리공단 157
 
1.9%
대구경북경제자유구역청 98
 
1.2%
대구광역시 달성군 84
 
1.0%
Other values (6) 93
 
1.1%

Length

2023-12-13T04:51:18.854917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구성서산업단지관리공단 3391
34.6%
서대구산업단지관리공단 1456
14.8%
대구제3산업단지관리공단 995
 
10.1%
사)대구검단산업단지관리공단 726
 
7.4%
한국산업단지공단 552
 
5.6%
대구지역본부 552
 
5.6%
달성사무소 552
 
5.6%
달성1차산업단지관리공단 383
 
3.9%
연구개발특구지원본부 196
 
2.0%
대구기술사업화센터 196
 
2.0%
Other values (16) 806
 
8.2%

설립구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
일반산업단지
7525 
국가산업단지
 
387
농공단지
 
167
지식산업센터
 
37
외국인투자지역
 
6
Other values (2)
 
9

Length

Max length8
Median length6
Mean length5.9581847
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반산업단지 7525
92.5%
국가산업단지 387
 
4.8%
농공단지 167
 
2.1%
지식산업센터 37
 
0.5%
외국인투자지역 6
 
0.1%
일반 5
 
0.1%
도시첨단산업단지 4
 
< 0.1%

Length

2023-12-13T04:51:19.017030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:51:19.187100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반산업단지 7525
92.5%
국가산업단지 387
 
4.8%
농공단지 167
 
2.1%
지식산업센터 37
 
0.5%
외국인투자지역 6
 
0.1%
일반 5
 
0.1%
도시첨단산업단지 4
 
< 0.1%

사업유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
제조업
6921 
비제조업
1210 

Length

Max length4
Median length3
Mean length3.1488132
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제조업
2nd row비제조업
3rd row제조업
4th row제조업
5th row제조업

Common Values

ValueCountFrequency (%)
제조업 6921
85.1%
비제조업 1210
 
14.9%

Length

2023-12-13T04:51:19.368714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:51:19.486288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 6921
85.1%
비제조업 1210
 
14.9%

남종업원
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct185
Distinct (%)2.5%
Missing799
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean15.198582
Minimum0
Maximum7210
Zeros211
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size71.6 KiB
2023-12-13T04:51:19.623191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q312
95-th percentile46
Maximum7210
Range7210
Interquartile range (IQR)9

Descriptive statistics

Standard deviation94.882129
Coefficient of variation (CV)6.2428279
Kurtosis4529.5674
Mean15.198582
Median Absolute Deviation (MAD)3
Skewness61.11017
Sum111436
Variance9002.6183
MonotonicityNot monotonic
2023-12-13T04:51:19.806252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 834
 
10.3%
3 777
 
9.6%
1 742
 
9.1%
4 605
 
7.4%
5 559
 
6.9%
6 409
 
5.0%
7 341
 
4.2%
8 313
 
3.8%
10 255
 
3.1%
0 211
 
2.6%
Other values (175) 2286
28.1%
(Missing) 799
 
9.8%
ValueCountFrequency (%)
0 211
 
2.6%
1 742
9.1%
2 834
10.3%
3 777
9.6%
4 605
7.4%
5 559
6.9%
6 409
5.0%
7 341
4.2%
8 313
 
3.8%
9 203
 
2.5%
ValueCountFrequency (%)
7210 1
< 0.1%
1643 1
< 0.1%
1000 2
< 0.1%
826 1
< 0.1%
772 1
< 0.1%
760 1
< 0.1%
738 1
< 0.1%
680 1
< 0.1%
656 1
< 0.1%
620 1
< 0.1%

여종업원
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct94
Distinct (%)1.5%
Missing1820
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean5.6119474
Minimum0
Maximum1000
Zeros751
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size71.6 KiB
2023-12-13T04:51:19.977984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile20
Maximum1000
Range1000
Interquartile range (IQR)4

Descriptive statistics

Standard deviation17.971481
Coefficient of variation (CV)3.2023608
Kurtosis1563.2868
Mean5.6119474
Median Absolute Deviation (MAD)1
Skewness31.534988
Sum35417
Variance322.97411
MonotonicityNot monotonic
2023-12-13T04:51:20.111559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1747
21.5%
2 991
12.2%
0 751
9.2%
3 614
 
7.6%
5 349
 
4.3%
4 343
 
4.2%
6 235
 
2.9%
10 158
 
1.9%
7 158
 
1.9%
8 136
 
1.7%
Other values (84) 829
10.2%
(Missing) 1820
22.4%
ValueCountFrequency (%)
0 751
9.2%
1 1747
21.5%
2 991
12.2%
3 614
 
7.6%
4 343
 
4.2%
5 349
 
4.3%
6 235
 
2.9%
7 158
 
1.9%
8 136
 
1.7%
9 100
 
1.2%
ValueCountFrequency (%)
1000 1
< 0.1%
415 1
< 0.1%
374 1
< 0.1%
176 1
< 0.1%
170 1
< 0.1%
150 1
< 0.1%
143 1
< 0.1%
142 1
< 0.1%
137 1
< 0.1%
132 1
< 0.1%

외국인(남)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct26
Distinct (%)0.9%
Missing5093
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean0.79098091
Minimum0
Maximum303
Zeros2626
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size71.6 KiB
2023-12-13T04:51:20.246839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum303
Range303
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.08983
Coefficient of variation (CV)7.699086
Kurtosis2002.021
Mean0.79098091
Median Absolute Deviation (MAD)0
Skewness40.916591
Sum2403
Variance37.08603
MonotonicityNot monotonic
2023-12-13T04:51:20.383634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2626
32.3%
1 93
 
1.1%
3 59
 
0.7%
2 57
 
0.7%
5 44
 
0.5%
4 30
 
0.4%
6 25
 
0.3%
8 18
 
0.2%
10 18
 
0.2%
7 15
 
0.2%
Other values (16) 53
 
0.7%
(Missing) 5093
62.6%
ValueCountFrequency (%)
0 2626
32.3%
1 93
 
1.1%
2 57
 
0.7%
3 59
 
0.7%
4 30
 
0.4%
5 44
 
0.5%
6 25
 
0.3%
7 15
 
0.2%
8 18
 
0.2%
9 10
 
0.1%
ValueCountFrequency (%)
303 1
 
< 0.1%
60 1
 
< 0.1%
39 1
 
< 0.1%
28 1
 
< 0.1%
22 1
 
< 0.1%
20 3
< 0.1%
19 2
< 0.1%
18 3
< 0.1%
17 2
< 0.1%
16 3
< 0.1%

외국인(여)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)0.5%
Missing5245
Missing (%)64.5%
Infinite0
Infinite (%)0.0%
Mean0.14414414
Minimum0
Maximum29
Zeros2746
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size71.6 KiB
2023-12-13T04:51:20.507791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0376009
Coefficient of variation (CV)7.1983562
Kurtosis325.31091
Mean0.14414414
Median Absolute Deviation (MAD)0
Skewness15.420209
Sum416
Variance1.0766156
MonotonicityNot monotonic
2023-12-13T04:51:20.645072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 2746
33.8%
1 54
 
0.7%
2 37
 
0.5%
3 18
 
0.2%
4 12
 
0.1%
5 6
 
0.1%
6 3
 
< 0.1%
20 2
 
< 0.1%
8 2
 
< 0.1%
12 1
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 5245
64.5%
ValueCountFrequency (%)
0 2746
33.8%
1 54
 
0.7%
2 37
 
0.5%
3 18
 
0.2%
4 12
 
0.1%
5 6
 
0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
20 2
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
8 2
 
< 0.1%
7 1
 
< 0.1%
6 3
< 0.1%
5 6
0.1%

종업원수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct214
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.407576
Minimum0
Maximum7225
Zeros888
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size71.6 KiB
2023-12-13T04:51:20.782514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q316
95-th percentile60
Maximum7225
Range7225
Interquartile range (IQR)13

Descriptive statistics

Standard deviation95.967537
Coefficient of variation (CV)5.2134804
Kurtosis3956.0893
Mean18.407576
Median Absolute Deviation (MAD)5
Skewness55.036374
Sum149672
Variance9209.7682
MonotonicityNot monotonic
2023-12-13T04:51:20.945781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 888
 
10.9%
3 587
 
7.2%
4 586
 
7.2%
2 579
 
7.1%
5 494
 
6.1%
6 433
 
5.3%
1 417
 
5.1%
7 384
 
4.7%
8 331
 
4.1%
10 318
 
3.9%
Other values (204) 3114
38.3%
ValueCountFrequency (%)
0 888
10.9%
1 417
5.1%
2 579
7.1%
3 587
7.2%
4 586
7.2%
5 494
6.1%
6 433
5.3%
7 384
4.7%
8 331
 
4.1%
9 246
 
3.0%
ValueCountFrequency (%)
7225 1
< 0.1%
2000 1
< 0.1%
1786 1
< 0.1%
1043 1
< 0.1%
1030 1
< 0.1%
876 1
< 0.1%
864 1
< 0.1%
851 1
< 0.1%
819 1
< 0.1%
730 1
< 0.1%
Distinct5553
Distinct (%)68.8%
Missing61
Missing (%)0.8%
Memory size63.7 KiB
2023-12-13T04:51:21.274773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length84
Median length48
Mean length7.7437423
Min length1

Characters and Unicode

Total characters62492
Distinct characters771
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4929 ?
Unique (%)61.1%

Sample

1st row실린더+댐퍼
2nd row경영 컨설팅
3rd row스케치북+수첩 등
4th row쓰레기종량제봉투 외
5th row자동차상업인쇄
ValueCountFrequency (%)
321
 
2.7%
자동차부품 313
 
2.6%
213
 
1.8%
자동차 191
 
1.6%
부품 174
 
1.5%
금형 164
 
1.4%
임대업 138
 
1.2%
임대 134
 
1.1%
124
 
1.0%
기계부품 111
 
0.9%
Other values (5891) 10076
84.3%
2023-12-13T04:51:22.035834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3981
 
6.4%
+ 2403
 
3.8%
2207
 
3.5%
1907
 
3.1%
1764
 
2.8%
1686
 
2.7%
1642
 
2.6%
1204
 
1.9%
1074
 
1.7%
885
 
1.4%
Other values (761) 43739
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52359
83.8%
Space Separator 3981
 
6.4%
Math Symbol 2403
 
3.8%
Uppercase Letter 1995
 
3.2%
Lowercase Letter 792
 
1.3%
Open Punctuation 374
 
0.6%
Close Punctuation 373
 
0.6%
Other Punctuation 130
 
0.2%
Decimal Number 65
 
0.1%
Dash Punctuation 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2207
 
4.2%
1907
 
3.6%
1764
 
3.4%
1686
 
3.2%
1642
 
3.1%
1204
 
2.3%
1074
 
2.1%
885
 
1.7%
830
 
1.6%
815
 
1.6%
Other values (690) 38345
73.2%
Uppercase Letter
ValueCountFrequency (%)
C 202
 
10.1%
P 187
 
9.4%
L 181
 
9.1%
E 175
 
8.8%
D 137
 
6.9%
T 133
 
6.7%
R 116
 
5.8%
S 100
 
5.0%
O 93
 
4.7%
A 89
 
4.5%
Other values (16) 582
29.2%
Lowercase Letter
ValueCountFrequency (%)
e 92
11.6%
a 72
 
9.1%
l 67
 
8.5%
t 63
 
8.0%
r 61
 
7.7%
o 61
 
7.7%
n 45
 
5.7%
i 43
 
5.4%
s 43
 
5.4%
c 39
 
4.9%
Other values (15) 206
26.0%
Decimal Number
ValueCountFrequency (%)
2 18
27.7%
0 10
15.4%
3 10
15.4%
8 6
 
9.2%
1 5
 
7.7%
4 5
 
7.7%
9 4
 
6.2%
5 3
 
4.6%
6 3
 
4.6%
7 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 69
53.1%
/ 50
38.5%
' 5
 
3.8%
& 3
 
2.3%
· 3
 
2.3%
Space Separator
ValueCountFrequency (%)
3981
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2403
100.0%
Open Punctuation
ValueCountFrequency (%)
( 374
100.0%
Close Punctuation
ValueCountFrequency (%)
) 373
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52359
83.8%
Common 7346
 
11.8%
Latin 2787
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2207
 
4.2%
1907
 
3.6%
1764
 
3.4%
1686
 
3.2%
1642
 
3.1%
1204
 
2.3%
1074
 
2.1%
885
 
1.7%
830
 
1.6%
815
 
1.6%
Other values (690) 38345
73.2%
Latin
ValueCountFrequency (%)
C 202
 
7.2%
P 187
 
6.7%
L 181
 
6.5%
E 175
 
6.3%
D 137
 
4.9%
T 133
 
4.8%
R 116
 
4.2%
S 100
 
3.6%
O 93
 
3.3%
e 92
 
3.3%
Other values (41) 1371
49.2%
Common
ValueCountFrequency (%)
3981
54.2%
+ 2403
32.7%
( 374
 
5.1%
) 373
 
5.1%
. 69
 
0.9%
/ 50
 
0.7%
- 20
 
0.3%
2 18
 
0.2%
0 10
 
0.1%
3 10
 
0.1%
Other values (10) 38
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52356
83.8%
ASCII 10130
 
16.2%
None 3
 
< 0.1%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3981
39.3%
+ 2403
23.7%
( 374
 
3.7%
) 373
 
3.7%
C 202
 
2.0%
P 187
 
1.8%
L 181
 
1.8%
E 175
 
1.7%
D 137
 
1.4%
T 133
 
1.3%
Other values (60) 1984
19.6%
Hangul
ValueCountFrequency (%)
2207
 
4.2%
1907
 
3.6%
1764
 
3.4%
1686
 
3.2%
1642
 
3.1%
1204
 
2.3%
1074
 
2.1%
885
 
1.7%
830
 
1.6%
815
 
1.6%
Other values (688) 38342
73.2%
None
ValueCountFrequency (%)
· 3
100.0%
Compat Jamo
ValueCountFrequency (%)
2
66.7%
1
33.3%

주원자재
Text

MISSING 

Distinct2502
Distinct (%)54.5%
Missing3542
Missing (%)43.6%
Memory size63.7 KiB
2023-12-13T04:51:22.487627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length43
Mean length6.1699717
Min length1

Characters and Unicode

Total characters28314
Distinct characters599
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2176 ?
Unique (%)47.4%

Sample

1st row지류
2nd row폴리에틸렌
3rd row알루미늄+폴리에스터 필름
4th row금속
5th row도금
ValueCountFrequency (%)
266
 
4.7%
232
 
4.1%
철판 182
 
3.2%
금속 182
 
3.2%
원사 116
 
2.0%
원단 108
 
1.9%
철강 107
 
1.9%
알루미늄 106
 
1.9%
염.조제 65
 
1.1%
59
 
1.0%
Other values (2554) 4238
74.9%
2023-12-13T04:51:23.294932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 2615
 
9.2%
1268
 
4.5%
1094
 
3.9%
850
 
3.0%
667
 
2.4%
454
 
1.6%
430
 
1.5%
P 394
 
1.4%
381
 
1.3%
359
 
1.3%
Other values (589) 19802
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19938
70.4%
Uppercase Letter 2832
 
10.0%
Math Symbol 2616
 
9.2%
Space Separator 1094
 
3.9%
Lowercase Letter 997
 
3.5%
Decimal Number 431
 
1.5%
Other Punctuation 147
 
0.5%
Open Punctuation 116
 
0.4%
Close Punctuation 116
 
0.4%
Dash Punctuation 26
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1268
 
6.4%
850
 
4.3%
667
 
3.3%
454
 
2.3%
430
 
2.2%
381
 
1.9%
359
 
1.8%
357
 
1.8%
354
 
1.8%
344
 
1.7%
Other values (515) 14474
72.6%
Uppercase Letter
ValueCountFrequency (%)
P 394
13.9%
S 330
11.7%
C 312
11.0%
E 249
 
8.8%
L 242
 
8.5%
B 138
 
4.9%
A 135
 
4.8%
T 125
 
4.4%
D 119
 
4.2%
O 103
 
3.6%
Other values (16) 685
24.2%
Lowercase Letter
ValueCountFrequency (%)
e 150
15.0%
l 94
9.4%
p 90
9.0%
s 86
 
8.6%
t 79
 
7.9%
o 61
 
6.1%
r 59
 
5.9%
c 56
 
5.6%
i 52
 
5.2%
a 48
 
4.8%
Other values (13) 222
22.3%
Decimal Number
ValueCountFrequency (%)
4 98
22.7%
0 96
22.3%
1 57
13.2%
5 54
12.5%
3 44
10.2%
6 33
 
7.7%
2 26
 
6.0%
7 13
 
3.0%
8 9
 
2.1%
9 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 121
82.3%
/ 20
 
13.6%
& 2
 
1.4%
' 2
 
1.4%
* 1
 
0.7%
% 1
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 2615
> 99.9%
~ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 115
99.1%
[ 1
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 115
99.1%
] 1
 
0.9%
Space Separator
ValueCountFrequency (%)
1094
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19938
70.4%
Common 4547
 
16.1%
Latin 3829
 
13.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1268
 
6.4%
850
 
4.3%
667
 
3.3%
454
 
2.3%
430
 
2.2%
381
 
1.9%
359
 
1.8%
357
 
1.8%
354
 
1.8%
344
 
1.7%
Other values (515) 14474
72.6%
Latin
ValueCountFrequency (%)
P 394
 
10.3%
S 330
 
8.6%
C 312
 
8.1%
E 249
 
6.5%
L 242
 
6.3%
e 150
 
3.9%
B 138
 
3.6%
A 135
 
3.5%
T 125
 
3.3%
D 119
 
3.1%
Other values (39) 1635
42.7%
Common
ValueCountFrequency (%)
+ 2615
57.5%
1094
24.1%
. 121
 
2.7%
( 115
 
2.5%
) 115
 
2.5%
4 98
 
2.2%
0 96
 
2.1%
1 57
 
1.3%
5 54
 
1.2%
3 44
 
1.0%
Other values (15) 138
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19935
70.4%
ASCII 8376
29.6%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 2615
31.2%
1094
13.1%
P 394
 
4.7%
S 330
 
3.9%
C 312
 
3.7%
E 249
 
3.0%
L 242
 
2.9%
e 150
 
1.8%
B 138
 
1.6%
A 135
 
1.6%
Other values (64) 2717
32.4%
Hangul
ValueCountFrequency (%)
1268
 
6.4%
850
 
4.3%
667
 
3.3%
454
 
2.3%
430
 
2.2%
381
 
1.9%
359
 
1.8%
357
 
1.8%
354
 
1.8%
344
 
1.7%
Other values (513) 14471
72.6%
Compat Jamo
ValueCountFrequency (%)
2
66.7%
1
33.3%

Interactions

2023-12-13T04:51:11.390899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:05.559143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:06.633311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:07.801603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.798407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:09.679937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.583769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.502916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:05.715874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:06.780947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:07.952019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.910606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:09.832506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.705298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.621330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:05.919412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:06.902065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.089807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.999576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:09.937653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.809278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.755597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:06.064658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:07.041122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.244312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:09.113243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.066720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.929475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.877436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:06.189419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:07.154116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.385482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:09.232962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.195566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.062834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.986721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:06.326973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:07.266259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.506250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:09.368225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.305394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.171037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:12.126688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:06.477308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:07.645474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:08.652589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:09.561370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:10.437700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:51:11.269626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:51:23.484753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번단지명지식산업센터명대표업종번호관할조직명설립구분사업유형남종업원여종업원외국인(남)외국인(여)종업원수
순번1.0000.2190.3610.1440.1950.0960.0820.0110.0310.0000.0690.023
단지명0.2191.0001.0000.6170.9800.9840.5410.1310.0480.4430.3890.153
지식산업센터명0.3611.0001.0000.5901.0000.6930.756NaNNaNNaN0.612NaN
대표업종번호0.1440.6170.5901.0000.5250.1650.9980.0000.6000.0000.0000.004
관할조직명0.1950.9801.0000.5251.0000.8710.5660.1610.0000.3370.2800.175
설립구분0.0960.9840.6930.1650.8711.0000.0920.0180.0000.0000.0590.044
사업유형0.0820.5410.7560.9980.5660.0921.0000.0000.0390.0000.0000.010
남종업원0.0110.131NaN0.0000.1610.0180.0001.0000.4220.0000.0000.988
여종업원0.0310.048NaN0.6000.0000.0000.0390.4221.0000.0000.2120.629
외국인(남)0.0000.443NaN0.0000.3370.0000.0000.0000.0001.0000.6550.000
외국인(여)0.0690.3890.6120.0000.2800.0590.0000.0000.2120.6551.0000.000
종업원수0.0230.153NaN0.0040.1750.0440.0100.9880.6290.0000.0001.000
2023-12-13T04:51:24.120364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지식산업센터명사업유형설립구분단지명관할조직명
지식산업센터명1.0000.6080.5520.9910.991
사업유형0.6081.0000.0980.4770.448
설립구분0.5520.0981.0000.8160.646
단지명0.9910.4770.8161.0000.838
관할조직명0.9910.4480.6460.8381.000
2023-12-13T04:51:24.256806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번대표업종번호남종업원여종업원외국인(남)외국인(여)종업원수단지명지식산업센터명관할조직명설립구분사업유형
순번1.0000.036-0.206-0.135-0.053-0.031-0.2090.0820.1210.0770.0480.063
대표업종번호0.0361.000-0.000-0.190-0.0020.011-0.1750.2800.2240.2360.0840.963
남종업원-0.206-0.0001.0000.5550.1240.0410.9390.0711.0000.0760.0120.000
여종업원-0.135-0.1900.5551.0000.1630.1560.7580.0001.0000.0000.0000.050
외국인(남)-0.053-0.0020.1240.1631.0000.5990.2260.2281.0000.2090.0000.000
외국인(여)-0.0310.0110.0410.1560.5991.0000.1300.1840.4320.1450.0370.000
종업원수-0.209-0.1750.9390.7580.2260.1301.0000.0831.0000.0830.0300.007
단지명0.0820.2800.0710.0000.2280.1840.0831.0000.9910.8380.8160.477
지식산업센터명0.1210.2241.0001.0001.0000.4321.0000.9911.0000.9910.5520.608
관할조직명0.0770.2360.0760.0000.2090.1450.0830.8380.9911.0000.6460.448
설립구분0.0480.0840.0120.0000.0000.0370.0300.8160.5520.6461.0000.098
사업유형0.0630.9630.0000.0500.0000.0000.0070.4770.6080.4480.0981.000

Missing values

2023-12-13T04:51:12.315893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:51:12.669093image/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.
2023-12-13T04:51:13.024372image/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

순번단지명회사명지식산업센터명공장대표주소(도로명)공장대표주소(지번)대표업종번호업종번호업종명관할조직명설립구분사업유형남종업원여종업원외국인(남)외국인(여)종업원수생산품주원자재
01성서지방산업단지( 주)명장<NA>대구광역시 달서구 호산동로7길 42, (3차단지 95B 1-7L) (호림동)대구광역시 달서구 호림동 2-18번지3039930399+29294+30391+30392+30400그 외 자동차용 신품 부품 제조업 외 4 종대구성서산업단지관리공단일반산업단지제조업277<NA><NA>34실린더+댐퍼<NA>
12서대구일반산업단지(SM)컨설팅디센터 1976대구광역시 서구 와룡로 307, 1층 104호(중리동) 1층 104호대구광역시 서구 중리동 1166-1 1층 104호 1층 104호6811268112비주거용 건물 임대업서대구산업단지관리공단일반산업단지비제조업00<NA><NA>0경영 컨설팅<NA>
23대구제3일반산업단지(사)한국장애인문화협회<NA>대구광역시 북구 노원로1길 160 (노원동3가)대구광역시 북구 노원동3가 39번지1790117901+18111+18113+18119문구용 종이제품 제조업 외 3 종대구제3산업단지관리공단일반산업단지제조업30003스케치북+수첩 등지류
34달성2차일반산업단지(사)한국지체장애인협회 달성군장애인재활자립작업장<NA>대구광역시 달성군 구지면 달성2차로 221대구광역시 달성군 구지면 예현리 757-12 달성군장애인재활자립작업장1629216292+18119+22231+32029+33933장식용 목제품 제조업 외 4 종한국산업단지공단 대구지역본부 달성사무소일반산업단지제조업37210058쓰레기종량제봉투 외폴리에틸렌
45성서5차첨단산업단지(유)대구특수금속<NA>대구광역시 달성군 다사읍 세천로3길 69, 성서5차첨단산업단지 6B 4L대구광역시 달성군 다사읍 세천리 1670-4번지 성서5차첨단산업단지 6B 4L2929429294주형 및 금형 제조업대구성서산업단지관리공단일반산업단지제조업14143<NA><NA>184자동차상업인쇄<NA>
56성서지방산업단지(유)대구특수금속<NA>대구광역시 달서구 성서공단북로69길 39, 1차단지 35B 7L 1차단지 35B 7L (갈산동)대구광역시 달서구 갈산동 100-62번지 1차단지 35B 7L3039930399+18112+26295+26299+29294+30391+30392+30400그 외 자동차용 신품 부품 제조업 외 7 종대구성서산업단지관리공단일반산업단지제조업54110065자동차부품+명판+라벨류알루미늄+폴리에스터 필름
67대구염색일반산업단지(유)대안에이엔씨<NA>대구광역시 서구 염색공단로21길 8, (유)대안에이엔씨 (비산동)대구광역시 서구 비산동 3194-6번지 (유)대안에이엔씨1340313403날염 가공업대구염색산업단지관리공단일반산업단지제조업2834<NA>35나염<NA>
78성서지방산업단지(유)딘텍스코리아<NA>대구광역시 달서구 성서공단남로32길 50, (2차단지 44B 6L) (월암동)대구광역시 달서구 월암동 922-5번지 (2차단지 44B 6L)1811118111+18113경 인쇄업 외 1 종대구성서산업단지관리공단일반산업단지제조업194<NA><NA>23경인쇄<NA>
89성서지방산업단지(유)시브코리아<NA>대구광역시 달서구 성서서로25안길 40, 2차단지 8B 2L (갈산동)대구광역시 달서구 갈산동 358-57번지 2차단지 8B 2L3192031920+31999모터사이클 제조업 외 1 종대구성서산업단지관리공단일반산업단지제조업21<NA><NA>3모터사이클<NA>
910대구제3일반산업단지(유)케이엠에스<NA>대구광역시 북구 노원로 157 (노원동3가)대구광역시 북구 노원동3가 31-82593325933비동력식 수공구 제조업대구제3산업단지관리공단일반산업단지제조업326<NA><NA>38툴홀더+보링바금속
순번단지명회사명지식산업센터명공장대표주소(도로명)공장대표주소(지번)대표업종번호업종번호업종명관할조직명설립구분사업유형남종업원여종업원외국인(남)외국인(여)종업원수생산품주원자재
81218122달성일반산업단지흥진섬유<NA>대구광역시 달성군 논공읍 논공로117길 40대구광역시 달성군 논공읍 북리 1-122번지6811268112비주거용 건물 임대업달성1차산업단지관리공단일반산업단지비제조업5110016연사원사
81228123대구제3일반산업단지흥진하이텍<NA>대구광역시 북구 3공단로 210-2 (노원동3가)대구광역시 북구 노원동3가 53-4번지3039930399+30391+30392+30400그 외 자동차용 신품 부품 제조업 외 3 종대구제3산업단지관리공단일반산업단지제조업21003자동차부품철판
81238124성서지방산업단지흥창정밀성서드림타운대구광역시 달서구 성서공단남로 37, 1층 123 (월암동)대구광역시 달서구 월암동 1-224번지 1층 1232592425924절삭가공 및 유사처리업대구성서산업단지관리공단일반산업단지제조업11<NA><NA>2절삭가공<NA>
81248125성서지방산업단지희성글로벌<NA>대구광역시 달서구 성서로35길 49, 2차단지 23-2B 12-1L (월암동)대구광역시 달서구 월암동 1-218번지 2차단지 23-2B 12-1L1399913999그 외 기타 분류 안된 섬유제품 제조업대구성서산업단지관리공단일반산업단지제조업1910020편조원단<NA>
81258126성서지방산업단지희성전자(주)대구2공장<NA>대구광역시 달서구 성서공단북로 61, (3차단지 60B 5L) (호산동)대구광역시 달서구 호산동 357-70번지 (3차단지 60B 5L)2621126211액정 표시장치 제조업대구성서산업단지관리공단일반산업단지제조업50200070액정표시장치
81268127성서지방산업단지희성전자(주)대구공장<NA>대구광역시 달서구 성서공단로11길 63, (3차단지 81B 1L) (호산동)대구광역시 달서구 호산동 710번지2621126211+28410+28422액정 표시장치 제조업 외 2 종대구성서산업단지관리공단일반산업단지제조업656374001030B L U+LED조명장치
81278128대구제3일반산업단지희창기계<NA>대구광역시 북구 노원로9길 70 (노원동3가)대구광역시 북구 노원동3가 200-5번지2926129261산업용 섬유 세척, 염색, 정리 및 가공 기계 제조업대구제3산업단지관리공단일반산업단지제조업20002섬유기계철재류
81288129대구제3일반산업단지희현테크<NA>대구광역시 북구 3공단로 97-12 (노원동3가)대구광역시 북구 노원동3가 244-122592425924절삭가공 및 유사처리업대구제3산업단지관리공단일반산업단지제조업<NA>1<NA><NA>1기계부품금속
81298130성서지방산업단지힉스정밀<NA>대구광역시 달서구 성서공단남로 170, 2차단지 39B 4L (월암동)대구광역시 달서구 월암동 916-3번지 2차단지 39B 4L2914229142기어 및 동력전달장치 제조업대구성서산업단지관리공단일반산업단지제조업71<NA><NA>8풀리(PULLEY)<NA>
81308131대구제3일반산업단지힐링워커<NA>대구광역시 북구 3공단로33길 27-2(노원동3가)대구광역시 북구 노원동3가 59-82921029210+30399+31202+31322농업 및 임업용 기계 제조업 외 3 종대구제3산업단지관리공단일반산업단지제조업<NA><NA><NA><NA>0프레스금형금속