Overview

Dataset statistics

Number of variables18
Number of observations1261
Missing cells1830
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory188.5 KiB
Average record size in memory153.1 B

Variable types

Numeric9
Categorical1
Text8

Dataset

Description진주시 관내에 제조업체로 최초로 등록된 공장 등록자료입니다. 공장등록시스템에서 내려 받은 자료입니다.제조업체명, 주소, 전화번호, 팩스번호, 종업원수 , 업종명, 생산품에 대한 최초의 등록 내용이므로 참고하시기 바랍니다.
Author경상남도 진주시
URLhttps://www.data.go.kr/data/15034937/fileData.do

Alerts

남종업원 is highly overall correlated with 종업원수 and 3 other fieldsHigh correlation
여종업원 is highly overall correlated with 종업원수High correlation
외국인(남) is highly overall correlated with 외국인(여)High correlation
외국인(여) is highly overall correlated with 외국인(남)High correlation
종업원수 is highly overall correlated with 남종업원 and 4 other fieldsHigh correlation
용지면적 is highly overall correlated with 남종업원 and 3 other fieldsHigh correlation
제조시설면적 is highly overall correlated with 남종업원 and 3 other fieldsHigh correlation
부대시설면적 is highly overall correlated with 남종업원 and 3 other fieldsHigh correlation
전화번호 has 98 (7.8%) missing valuesMissing
팩스번호 has 104 (8.2%) missing valuesMissing
남종업원 has 25 (2.0%) missing valuesMissing
여종업원 has 215 (17.0%) missing valuesMissing
외국인(남) has 652 (51.7%) missing valuesMissing
외국인(여) has 725 (57.5%) missing valuesMissing
순번 has unique valuesUnique
여종업원 has 121 (9.6%) zerosZeros
외국인(남) has 424 (33.6%) zerosZeros
외국인(여) has 483 (38.3%) zerosZeros
용지면적 has 218 (17.3%) zerosZeros
부대시설면적 has 250 (19.8%) zerosZeros

Reproduction

Analysis started2024-03-14 13:55:37.888069
Analysis finished2024-03-14 13:55:59.672921
Duration21.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct1261
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631
Minimum1
Maximum1261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:55:59.893399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile64
Q1316
median631
Q3946
95-th percentile1198
Maximum1261
Range1260
Interquartile range (IQR)630

Descriptive statistics

Standard deviation364.16365
Coefficient of variation (CV)0.57712148
Kurtosis-1.2
Mean631
Median Absolute Deviation (MAD)315
Skewness0
Sum795691
Variance132615.17
MonotonicityStrictly increasing
2024-03-14T22:56:00.471019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
839 1
 
0.1%
846 1
 
0.1%
845 1
 
0.1%
844 1
 
0.1%
843 1
 
0.1%
842 1
 
0.1%
841 1
 
0.1%
840 1
 
0.1%
838 1
 
0.1%
Other values (1251) 1251
99.2%
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 (%)
1261 1
0.1%
1260 1
0.1%
1259 1
0.1%
1258 1
0.1%
1257 1
0.1%
1256 1
0.1%
1255 1
0.1%
1254 1
0.1%
1253 1
0.1%
1252 1
0.1%

단지명
Categorical

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
<NA>
516 
진주상평일반산업단지
425 
진주정촌일반산업단지
97 
진주일반산업단지
69 
진주뿌리일반산업단지
 
44
Other values (7)
110 

Length

Max length12
Median length10
Mean length7.3838224
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row진주일반산업단지
2nd row진주상평일반산업단지
3rd row진주상평일반산업단지
4th row진주상평일반산업단지
5th row진주생물산업전문농공단지

Common Values

ValueCountFrequency (%)
<NA> 516
40.9%
진주상평일반산업단지 425
33.7%
진주정촌일반산업단지 97
 
7.7%
진주일반산업단지 69
 
5.5%
진주뿌리일반산업단지 44
 
3.5%
진주실크전문농공단지 29
 
2.3%
진주생물산업전문농공단지 21
 
1.7%
진주사봉농공단지 17
 
1.3%
진주대곡농공단지 16
 
1.3%
진주진성농공단지 16
 
1.3%
Other values (2) 11
 
0.9%

Length

2024-03-14T22:56:00.923577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 516
40.9%
진주상평일반산업단지 425
33.7%
진주정촌일반산업단지 97
 
7.7%
진주일반산업단지 69
 
5.5%
진주뿌리일반산업단지 44
 
3.5%
진주실크전문농공단지 29
 
2.3%
진주생물산업전문농공단지 21
 
1.7%
진주사봉농공단지 17
 
1.3%
진주대곡농공단지 16
 
1.3%
진주진성농공단지 16
 
1.3%
Other values (2) 11
 
0.9%

회사명
Text

Distinct1212
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-14T22:56:01.880234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length6.5836638
Min length1

Characters and Unicode

Total characters8302
Distinct characters452
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

Unique1167 ?
Unique (%)92.5%

Sample

1st row(유)경남라이팅
2nd row(유)동양프라스틱
3rd row(유)유창이엔지
4th row(유)화신테크
5th row(주)HK바이오텍
ValueCountFrequency (%)
주식회사 91
 
6.3%
농업회사법인 17
 
1.2%
제2공장 9
 
0.6%
2공장 7
 
0.5%
진주공장 5
 
0.3%
3공장 4
 
0.3%
흥성공업(주 4
 
0.3%
진주지점 3
 
0.2%
주)성광 3
 
0.2%
주)오엔이 3
 
0.2%
Other values (1219) 1298
89.9%
2024-03-14T22:56:03.310840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 640
 
7.7%
( 471
 
5.7%
) 471
 
5.7%
업 288
 
3.5%
공 261
 
3.1%
사 238
 
2.9%
이 189
 
2.3%
184
 
2.2%
정 179
 
2.2%
진 148
 
1.8%
Other values (442) 5233
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7001
84.3%
Open Punctuation 471
 
5.7%
Close Punctuation 471
 
5.7%
Space Separator 184
 
2.2%
Uppercase Letter 99
 
1.2%
Decimal Number 50
 
0.6%
Other Punctuation 12
 
0.1%
Lowercase Letter 9
 
0.1%
Other Symbol 4
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 640
 
9.1%
업 288
 
4.1%
공 261
 
3.7%
사 238
 
3.4%
이 189
 
2.7%
정 179
 
2.6%
진 148
 
2.1%
산 142
 
2.0%
회 132
 
1.9%
성 127
 
1.8%
Other values (399) 4657
66.5%
Uppercase Letter
ValueCountFrequency (%)
E 14
14.1%
G 13
13.1%
N 12
12.1%
S 9
9.1%
T 7
 
7.1%
C 6
 
6.1%
D 6
 
6.1%
M 6
 
6.1%
P 4
 
4.0%
A 4
 
4.0%
Other values (11) 18
18.2%
Lowercase Letter
ValueCountFrequency (%)
o 2
22.2%
u 1
11.1%
l 1
11.1%
t 1
11.1%
e 1
11.1%
c 1
11.1%
h 1
11.1%
g 1
11.1%
Decimal Number
ValueCountFrequency (%)
2 30
60.0%
3 11
 
22.0%
0 3
 
6.0%
4 2
 
4.0%
8 2
 
4.0%
1 2
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 9
75.0%
& 2
 
16.7%
, 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 471
100.0%
Close Punctuation
ValueCountFrequency (%)
) 471
100.0%
Space Separator
ValueCountFrequency (%)
184
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7005
84.4%
Common 1189
 
14.3%
Latin 108
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 640
 
9.1%
업 288
 
4.1%
공 261
 
3.7%
사 238
 
3.4%
이 189
 
2.7%
정 179
 
2.6%
진 148
 
2.1%
산 142
 
2.0%
회 132
 
1.9%
성 127
 
1.8%
Other values (400) 4661
66.5%
Latin
ValueCountFrequency (%)
E 14
13.0%
G 13
12.0%
N 12
11.1%
S 9
 
8.3%
T 7
 
6.5%
C 6
 
5.6%
D 6
 
5.6%
M 6
 
5.6%
P 4
 
3.7%
A 4
 
3.7%
Other values (19) 27
25.0%
Common
ValueCountFrequency (%)
( 471
39.6%
) 471
39.6%
184
 
15.5%
2 30
 
2.5%
3 11
 
0.9%
. 9
 
0.8%
0 3
 
0.3%
4 2
 
0.2%
8 2
 
0.2%
1 2
 
0.2%
Other values (3) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7001
84.3%
ASCII 1297
 
15.6%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 640
 
9.1%
업 288
 
4.1%
공 261
 
3.7%
사 238
 
3.4%
이 189
 
2.7%
정 179
 
2.6%
진 148
 
2.1%
산 142
 
2.0%
회 132
 
1.9%
성 127
 
1.8%
Other values (399) 4657
66.5%
ASCII
ValueCountFrequency (%)
( 471
36.3%
) 471
36.3%
184
 
14.2%
2 30
 
2.3%
E 14
 
1.1%
G 13
 
1.0%
N 12
 
0.9%
3 11
 
0.8%
. 9
 
0.7%
S 9
 
0.7%
Other values (32) 73
 
5.6%
None
ValueCountFrequency (%)
㈜ 4
100.0%

공장대표주소(도로명)
Text

Distinct1091
Distinct (%)87.3%
Missing11
Missing (%)0.9%
Memory size10.0 KiB
2024-03-14T22:56:04.380426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length80
Median length59
Mean length27.372
Min length18

Characters and Unicode

Total characters34215
Distinct characters227
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

Unique963 ?
Unique (%)77.0%

Sample

1st row경상남도 진주시 사봉면 산업단지로 102
2nd row경상남도 진주시 동진로264번길 12 (상대동) 외 1필지
3rd row경상남도 진주시 돗골로58번길 19 (상평동)
4th row경상남도 진주시 남강로 1273 (상평동)
5th row경상남도 진주시 문산읍 월아산로950번길 6
ValueCountFrequency (%)
경상남도 1250
 
17.6%
진주시 1250
 
17.6%
상평동 356
 
5.0%
정촌면 142
 
2.0%
상대동 123
 
1.7%
문산읍 117
 
1.6%
외 116
 
1.6%
사봉면 104
 
1.5%
1필지 75
 
1.1%
12 68
 
1.0%
Other values (1019) 3503
49.3%
2024-03-14T22:56:05.993152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5855
 
17.1%
상 1789
 
5.2%
진 1507
 
4.4%
1 1490
 
4.4%
남 1366
 
4.0%
주 1325
 
3.9%
도 1306
 
3.8%
경 1298
 
3.8%
시 1253
 
3.7%
로 1173
 
3.4%
Other values (217) 15853
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20180
59.0%
Decimal Number 6063
 
17.7%
Space Separator 5855
 
17.1%
Open Punctuation 763
 
2.2%
Close Punctuation 758
 
2.2%
Dash Punctuation 263
 
0.8%
Other Punctuation 212
 
0.6%
Uppercase Letter 121
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1789
 
8.9%
진 1507
 
7.5%
남 1366
 
6.8%
주 1325
 
6.6%
도 1306
 
6.5%
경 1298
 
6.4%
시 1253
 
6.2%
로 1173
 
5.8%
동 1064
 
5.3%
길 755
 
3.7%
Other values (193) 7344
36.4%
Decimal Number
ValueCountFrequency (%)
1 1490
24.6%
2 752
12.4%
4 562
 
9.3%
5 555
 
9.2%
3 533
 
8.8%
0 504
 
8.3%
9 501
 
8.3%
6 433
 
7.1%
7 385
 
6.3%
8 348
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 55
45.5%
B 54
44.6%
C 6
 
5.0%
S 2
 
1.7%
I 1
 
0.8%
K 1
 
0.8%
D 1
 
0.8%
T 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 211
99.5%
& 1
 
0.5%
Space Separator
ValueCountFrequency (%)
5855
100.0%
Open Punctuation
ValueCountFrequency (%)
( 763
100.0%
Close Punctuation
ValueCountFrequency (%)
) 758
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20180
59.0%
Common 13914
40.7%
Latin 121
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1789
 
8.9%
진 1507
 
7.5%
남 1366
 
6.8%
주 1325
 
6.6%
도 1306
 
6.5%
경 1298
 
6.4%
시 1253
 
6.2%
로 1173
 
5.8%
동 1064
 
5.3%
길 755
 
3.7%
Other values (193) 7344
36.4%
Common
ValueCountFrequency (%)
5855
42.1%
1 1490
 
10.7%
( 763
 
5.5%
) 758
 
5.4%
2 752
 
5.4%
4 562
 
4.0%
5 555
 
4.0%
3 533
 
3.8%
0 504
 
3.6%
9 501
 
3.6%
Other values (6) 1641
 
11.8%
Latin
ValueCountFrequency (%)
A 55
45.5%
B 54
44.6%
C 6
 
5.0%
S 2
 
1.7%
I 1
 
0.8%
K 1
 
0.8%
D 1
 
0.8%
T 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20180
59.0%
ASCII 14035
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5855
41.7%
1 1490
 
10.6%
( 763
 
5.4%
) 758
 
5.4%
2 752
 
5.4%
4 562
 
4.0%
5 555
 
4.0%
3 533
 
3.8%
0 504
 
3.6%
9 501
 
3.6%
Other values (14) 1762
 
12.6%
Hangul
ValueCountFrequency (%)
상 1789
 
8.9%
진 1507
 
7.5%
남 1366
 
6.8%
주 1325
 
6.6%
도 1306
 
6.5%
경 1298
 
6.4%
시 1253
 
6.2%
로 1173
 
5.8%
동 1064
 
5.3%
길 755
 
3.7%
Other values (193) 7344
36.4%

공장대표주소(지번)
Text

Distinct1136
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-14T22:56:07.182064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length55
Mean length24.747819
Min length13

Characters and Unicode

Total characters31207
Distinct characters206
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

Unique1034 ?
Unique (%)82.0%

Sample

1st row경상남도 진주시 사봉면 사곡리 1850-2
2nd row경상남도 진주시 상대2동 313-2번지 외 1필지
3rd row경상남도 진주시 상평동 201-4번지
4th row경상남도 진주시 상평동 55-39번지
5th row경상남도 진주시 문산읍 이곡리 1182번지
ValueCountFrequency (%)
경상남도 1260
19.3%
진주시 1260
19.3%
상평동 378
 
5.8%
정촌면 150
 
2.3%
예하리 138
 
2.1%
상대동 135
 
2.1%
외 118
 
1.8%
문산읍 116
 
1.8%
사봉면 104
 
1.6%
사곡리 78
 
1.2%
Other values (1342) 2779
42.6%
2024-03-14T22:56:08.769051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5305
 
17.0%
상 1853
 
5.9%
1 1435
 
4.6%
진 1380
 
4.4%
주 1298
 
4.2%
경 1294
 
4.1%
남 1272
 
4.1%
시 1264
 
4.1%
도 1260
 
4.0%
지 1140
 
3.7%
Other values (196) 13706
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18361
58.8%
Decimal Number 6236
 
20.0%
Space Separator 5305
 
17.0%
Dash Punctuation 1095
 
3.5%
Uppercase Letter 117
 
0.4%
Open Punctuation 41
 
0.1%
Close Punctuation 36
 
0.1%
Other Punctuation 16
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1853
 
10.1%
진 1380
 
7.5%
주 1298
 
7.1%
경 1294
 
7.0%
남 1272
 
6.9%
시 1264
 
6.9%
도 1260
 
6.9%
지 1140
 
6.2%
번 956
 
5.2%
동 818
 
4.5%
Other values (173) 5826
31.7%
Decimal Number
ValueCountFrequency (%)
1 1435
23.0%
2 936
15.0%
3 880
14.1%
5 581
9.3%
0 555
 
8.9%
4 483
 
7.7%
6 450
 
7.2%
8 318
 
5.1%
7 304
 
4.9%
9 294
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
A 55
47.0%
B 52
44.4%
C 5
 
4.3%
S 2
 
1.7%
D 1
 
0.9%
T 1
 
0.9%
I 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
& 1
 
6.2%
Space Separator
ValueCountFrequency (%)
5305
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1095
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18361
58.8%
Common 12729
40.8%
Latin 117
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1853
 
10.1%
진 1380
 
7.5%
주 1298
 
7.1%
경 1294
 
7.0%
남 1272
 
6.9%
시 1264
 
6.9%
도 1260
 
6.9%
지 1140
 
6.2%
번 956
 
5.2%
동 818
 
4.5%
Other values (173) 5826
31.7%
Common
ValueCountFrequency (%)
5305
41.7%
1 1435
 
11.3%
- 1095
 
8.6%
2 936
 
7.4%
3 880
 
6.9%
5 581
 
4.6%
0 555
 
4.4%
4 483
 
3.8%
6 450
 
3.5%
8 318
 
2.5%
Other values (6) 691
 
5.4%
Latin
ValueCountFrequency (%)
A 55
47.0%
B 52
44.4%
C 5
 
4.3%
S 2
 
1.7%
D 1
 
0.9%
T 1
 
0.9%
I 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18361
58.8%
ASCII 12846
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5305
41.3%
1 1435
 
11.2%
- 1095
 
8.5%
2 936
 
7.3%
3 880
 
6.9%
5 581
 
4.5%
0 555
 
4.3%
4 483
 
3.8%
6 450
 
3.5%
8 318
 
2.5%
Other values (13) 808
 
6.3%
Hangul
ValueCountFrequency (%)
상 1853
 
10.1%
진 1380
 
7.5%
주 1298
 
7.1%
경 1294
 
7.0%
남 1272
 
6.9%
시 1264
 
6.9%
도 1260
 
6.9%
지 1140
 
6.2%
번 956
 
5.2%
동 818
 
4.5%
Other values (173) 5826
31.7%

업종번호
Text

Distinct519
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-14T22:56:10.068533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length250
Median length7
Mean length12.720856
Min length7

Characters and Unicode

Total characters16041
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique376 ?
Unique (%)29.8%

Sample

1st row26299, 25111, 25112, 25113, 25114, 25119, 25999, 26295, 26410, 26421, 28111, 28123, 28410, 28422, 28423, 28429, 28903
2nd row22232,
3rd row29210,
4th row23222, 23232
5th row10797, 10403
ValueCountFrequency (%)
25924 209
 
8.6%
29210 137
 
5.7%
30400 70
 
2.9%
29142 66
 
2.7%
30399 62
 
2.6%
25113 59
 
2.4%
25112 58
 
2.4%
30391 49
 
2.0%
30392 48
 
2.0%
31322 43
 
1.8%
Other values (301) 1620
66.9%
2024-03-14T22:56:11.887808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3365
21.0%
1 2315
14.4%
, 1968
12.3%
1968
12.3%
9 1714
10.7%
3 1460
9.1%
0 1144
 
7.1%
4 806
 
5.0%
5 720
 
4.5%
6 219
 
1.4%
Other values (2) 362
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12105
75.5%
Other Punctuation 1968
 
12.3%
Space Separator 1968
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3365
27.8%
1 2315
19.1%
9 1714
14.2%
3 1460
12.1%
0 1144
 
9.5%
4 806
 
6.7%
5 720
 
5.9%
6 219
 
1.8%
8 196
 
1.6%
7 166
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 1968
100.0%
Space Separator
ValueCountFrequency (%)
1968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16041
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3365
21.0%
1 2315
14.4%
, 1968
12.3%
1968
12.3%
9 1714
10.7%
3 1460
9.1%
0 1144
 
7.1%
4 806
 
5.0%
5 720
 
4.5%
6 219
 
1.4%
Other values (2) 362
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3365
21.0%
1 2315
14.4%
, 1968
12.3%
1968
12.3%
9 1714
10.7%
3 1460
9.1%
0 1144
 
7.1%
4 806
 
5.0%
5 720
 
4.5%
6 219
 
1.4%
Other values (2) 362
 
2.3%

업종명
Text

Distinct421
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-14T22:56:13.366879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length16.50912
Min length3

Characters and Unicode

Total characters20818
Distinct characters279
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

Unique245 ?
Unique (%)19.4%

Sample

1st row그 외 기타 전자부품 제조업 외 16 종
2nd row포장용 플라스틱 성형용기 제조업
3rd row농업 및 임업용 기계 제조업
4th row위생용 및 산업용 도자기 제조업 외 1 종
5th row건강기능식품 제조업 외 1 종
ValueCountFrequency (%)
제조업 939
 
14.1%
및 734
 
11.0%
외 553
 
8.3%
종 453
 
6.8%
1 225
 
3.4%
기타 205
 
3.1%
절삭가공 189
 
2.8%
유사처리업 189
 
2.8%
기계 127
 
1.9%
금속 123
 
1.8%
Other values (415) 2935
44.0%
2024-03-14T22:56:15.287402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5412
26.0%
업 1523
 
7.3%
제 1162
 
5.6%
조 1134
 
5.4%
및 735
 
3.5%
기 622
 
3.0%
외 563
 
2.7%
종 463
 
2.2%
용 416
 
2.0%
품 379
 
1.8%
Other values (269) 8409
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14818
71.2%
Space Separator 5412
 
26.0%
Decimal Number 472
 
2.3%
Other Punctuation 100
 
0.5%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 1523
 
10.3%
제 1162
 
7.8%
조 1134
 
7.7%
및 735
 
5.0%
기 622
 
4.2%
외 563
 
3.8%
종 463
 
3.1%
용 416
 
2.8%
품 379
 
2.6%
공 319
 
2.2%
Other values (255) 7502
50.6%
Decimal Number
ValueCountFrequency (%)
1 244
51.7%
2 72
 
15.3%
3 70
 
14.8%
4 39
 
8.3%
5 19
 
4.0%
6 12
 
2.5%
7 10
 
2.1%
9 3
 
0.6%
8 3
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 93
93.0%
. 7
 
7.0%
Space Separator
ValueCountFrequency (%)
5412
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14818
71.2%
Common 6000
28.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 1523
 
10.3%
제 1162
 
7.8%
조 1134
 
7.7%
및 735
 
5.0%
기 622
 
4.2%
외 563
 
3.8%
종 463
 
3.1%
용 416
 
2.8%
품 379
 
2.6%
공 319
 
2.2%
Other values (255) 7502
50.6%
Common
ValueCountFrequency (%)
5412
90.2%
1 244
 
4.1%
, 93
 
1.6%
2 72
 
1.2%
3 70
 
1.2%
4 39
 
0.7%
5 19
 
0.3%
6 12
 
0.2%
7 10
 
0.2%
( 8
 
0.1%
Other values (4) 21
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14810
71.1%
ASCII 6000
28.8%
Compat Jamo 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5412
90.2%
1 244
 
4.1%
, 93
 
1.6%
2 72
 
1.2%
3 70
 
1.2%
4 39
 
0.7%
5 19
 
0.3%
6 12
 
0.2%
7 10
 
0.2%
( 8
 
0.1%
Other values (4) 21
 
0.4%
Hangul
ValueCountFrequency (%)
업 1523
 
10.3%
제 1162
 
7.8%
조 1134
 
7.7%
및 735
 
5.0%
기 622
 
4.2%
외 563
 
3.8%
종 463
 
3.1%
용 416
 
2.8%
품 379
 
2.6%
공 319
 
2.2%
Other values (254) 7494
50.6%
Compat Jamo
ValueCountFrequency (%)
ㆍ 8
100.0%

전화번호
Text

MISSING 

Distinct1050
Distinct (%)90.3%
Missing98
Missing (%)7.8%
Memory size10.0 KiB
2024-03-14T22:56:16.266790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.033534
Min length12

Characters and Unicode

Total characters13995
Distinct characters14
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

Unique952 ?
Unique (%)81.9%

Sample

1st row055-795-6685
2nd row055-762-4588
3rd row055-753-9396
4th row055-755-6811
5th row055-762-9307
ValueCountFrequency (%)
055-762-0674 4
 
0.3%
055-762-5200 4
 
0.3%
055-758-7377 3
 
0.3%
055-749-3200 3
 
0.3%
055-758-1546 3
 
0.3%
055-758-4200 3
 
0.3%
055-758-1123 3
 
0.3%
055-758-5681 3
 
0.3%
055-759-6161 3
 
0.3%
055-752-2198 3
 
0.3%
Other values (1040) 1131
97.2%
2024-03-14T22:56:17.638357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3498
25.0%
- 2326
16.6%
0 1904
13.6%
7 1705
12.2%
2 742
 
5.3%
6 727
 
5.2%
1 661
 
4.7%
8 641
 
4.6%
4 628
 
4.5%
3 620
 
4.4%
Other values (4) 543
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11651
83.3%
Dash Punctuation 2326
 
16.6%
Uppercase Letter 18
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3498
30.0%
0 1904
16.3%
7 1705
14.6%
2 742
 
6.4%
6 727
 
6.2%
1 661
 
5.7%
8 641
 
5.5%
4 628
 
5.4%
3 620
 
5.3%
9 525
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
A 6
33.3%
R 6
33.3%
S 6
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13977
99.9%
Latin 18
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3498
25.0%
- 2326
16.6%
0 1904
13.6%
7 1705
12.2%
2 742
 
5.3%
6 727
 
5.2%
1 661
 
4.7%
8 641
 
4.6%
4 628
 
4.5%
3 620
 
4.4%
Latin
ValueCountFrequency (%)
A 6
33.3%
R 6
33.3%
S 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3498
25.0%
- 2326
16.6%
0 1904
13.6%
7 1705
12.2%
2 742
 
5.3%
6 727
 
5.2%
1 661
 
4.7%
8 641
 
4.6%
4 628
 
4.5%
3 620
 
4.4%
Other values (4) 543
 
3.9%

팩스번호
Text

MISSING 

Distinct997
Distinct (%)86.2%
Missing104
Missing (%)8.2%
Memory size10.0 KiB
2024-03-14T22:56:18.464660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.902334
Min length2

Characters and Unicode

Total characters13771
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

Unique883 ?
Unique (%)76.3%

Sample

1st row055-276-6686
2nd row055-757-1438
3rd row055-753-1036
4th row055-755-2288
5th row055-762-9407
ValueCountFrequency (%)
55 17
 
1.5%
055-741-2326 6
 
0.5%
055-755-1443 5
 
0.4%
055-795-1777 4
 
0.3%
055-762-0647 4
 
0.3%
055-757-4622 4
 
0.3%
055-744-2158 4
 
0.3%
055-758-5682 4
 
0.3%
055-758-5583 3
 
0.3%
055-744-2002 3
 
0.3%
Other values (987) 1103
95.3%
2024-03-14T22:56:19.496374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3386
24.6%
- 2272
16.5%
0 1800
13.1%
7 1608
11.7%
6 769
 
5.6%
2 754
 
5.5%
4 673
 
4.9%
8 663
 
4.8%
3 654
 
4.7%
1 627
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11499
83.5%
Dash Punctuation 2272
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3386
29.4%
0 1800
15.7%
7 1608
14.0%
6 769
 
6.7%
2 754
 
6.6%
4 673
 
5.9%
8 663
 
5.8%
3 654
 
5.7%
1 627
 
5.5%
9 565
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 2272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13771
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3386
24.6%
- 2272
16.5%
0 1800
13.1%
7 1608
11.7%
6 769
 
5.6%
2 754
 
5.5%
4 673
 
4.9%
8 663
 
4.8%
3 654
 
4.7%
1 627
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3386
24.6%
- 2272
16.5%
0 1800
13.1%
7 1608
11.7%
6 769
 
5.6%
2 754
 
5.5%
4 673
 
4.9%
8 663
 
4.8%
3 654
 
4.7%
1 627
 
4.6%

남종업원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)5.4%
Missing25
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean8.8309061
Minimum0
Maximum400
Zeros7
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:56:19.748009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q38
95-th percentile30
Maximum400
Range400
Interquartile range (IQR)6

Descriptive statistics

Standard deviation20.156466
Coefficient of variation (CV)2.2824913
Kurtosis168.85678
Mean8.8309061
Median Absolute Deviation (MAD)2
Skewness10.899179
Sum10915
Variance406.28313
MonotonicityNot monotonic
2024-03-14T22:56:20.014090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 182
14.4%
2 180
14.3%
3 162
12.8%
4 128
10.2%
5 100
7.9%
7 72
 
5.7%
6 60
 
4.8%
8 49
 
3.9%
10 38
 
3.0%
9 32
 
2.5%
Other values (57) 233
18.5%
(Missing) 25
 
2.0%
ValueCountFrequency (%)
0 7
 
0.6%
1 182
14.4%
2 180
14.3%
3 162
12.8%
4 128
10.2%
5 100
7.9%
6 60
 
4.8%
7 72
 
5.7%
8 49
 
3.9%
9 32
 
2.5%
ValueCountFrequency (%)
400 1
0.1%
312 1
0.1%
196 1
0.1%
176 1
0.1%
141 1
0.1%
116 1
0.1%
111 1
0.1%
101 1
0.1%
100 1
0.1%
96 1
0.1%

여종업원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)3.2%
Missing215
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean3.2122371
Minimum0
Maximum73
Zeros121
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:56:20.253480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile10
Maximum73
Range73
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.9639173
Coefficient of variation (CV)1.8566243
Kurtosis60.073029
Mean3.2122371
Median Absolute Deviation (MAD)1
Skewness6.6733266
Sum3360
Variance35.568309
MonotonicityNot monotonic
2024-03-14T22:56:20.542102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 371
29.4%
2 213
16.9%
0 121
 
9.6%
3 108
 
8.6%
4 52
 
4.1%
5 52
 
4.1%
6 23
 
1.8%
10 17
 
1.3%
8 16
 
1.3%
7 14
 
1.1%
Other values (23) 59
 
4.7%
(Missing) 215
17.0%
ValueCountFrequency (%)
0 121
 
9.6%
1 371
29.4%
2 213
16.9%
3 108
 
8.6%
4 52
 
4.1%
5 52
 
4.1%
6 23
 
1.8%
7 14
 
1.1%
8 16
 
1.3%
9 8
 
0.6%
ValueCountFrequency (%)
73 1
0.1%
70 1
0.1%
67 1
0.1%
60 1
0.1%
48 1
0.1%
40 2
0.2%
34 2
0.2%
30 1
0.1%
29 1
0.1%
25 2
0.2%

외국인(남)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)3.1%
Missing652
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean1.5385878
Minimum0
Maximum21
Zeros424
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:56:20.931415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8.6
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.1874918
Coefficient of variation (CV)2.0716996
Kurtosis8.8027321
Mean1.5385878
Median Absolute Deviation (MAD)0
Skewness2.7512953
Sum937
Variance10.160104
MonotonicityNot monotonic
2024-03-14T22:56:21.175044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 424
33.6%
1 33
 
2.6%
3 29
 
2.3%
4 27
 
2.1%
2 17
 
1.3%
6 15
 
1.2%
5 15
 
1.2%
7 10
 
0.8%
10 8
 
0.6%
8 8
 
0.6%
Other values (9) 23
 
1.8%
(Missing) 652
51.7%
ValueCountFrequency (%)
0 424
33.6%
1 33
 
2.6%
2 17
 
1.3%
3 29
 
2.3%
4 27
 
2.1%
5 15
 
1.2%
6 15
 
1.2%
7 10
 
0.8%
8 8
 
0.6%
9 7
 
0.6%
ValueCountFrequency (%)
21 2
 
0.2%
18 1
 
0.1%
16 1
 
0.1%
15 3
 
0.2%
14 2
 
0.2%
13 2
 
0.2%
12 3
 
0.2%
11 2
 
0.2%
10 8
0.6%
9 7
0.6%

외국인(여)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)1.5%
Missing725
Missing (%)57.5%
Infinite0
Infinite (%)0.0%
Mean0.17910448
Minimum0
Maximum9
Zeros483
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:56:21.356859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73726695
Coefficient of variation (CV)4.1164072
Kurtosis67.724337
Mean0.17910448
Median Absolute Deviation (MAD)0
Skewness7.1753504
Sum96
Variance0.54356256
MonotonicityNot monotonic
2024-03-14T22:56:21.531069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 483
38.3%
1 32
 
2.5%
2 14
 
1.1%
3 2
 
0.2%
4 2
 
0.2%
8 1
 
0.1%
5 1
 
0.1%
9 1
 
0.1%
(Missing) 725
57.5%
ValueCountFrequency (%)
0 483
38.3%
1 32
 
2.5%
2 14
 
1.1%
3 2
 
0.2%
4 2
 
0.2%
5 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
9 1
 
0.1%
8 1
 
0.1%
5 1
 
0.1%
4 2
 
0.2%
3 2
 
0.2%
2 14
 
1.1%
1 32
 
2.5%
0 483
38.3%

종업원수
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.139572
Minimum0
Maximum440
Zeros9
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:56:21.846807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q312
95-th percentile40
Maximum440
Range440
Interquartile range (IQR)9

Descriptive statistics

Standard deviation24.074643
Coefficient of variation (CV)1.9831542
Kurtosis120.67885
Mean12.139572
Median Absolute Deviation (MAD)3
Skewness9.0535319
Sum15308
Variance579.58844
MonotonicityNot monotonic
2024-03-14T22:56:22.261556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 146
11.6%
5 140
 
11.1%
4 126
 
10.0%
2 116
 
9.2%
6 74
 
5.9%
1 73
 
5.8%
10 64
 
5.1%
7 63
 
5.0%
8 57
 
4.5%
9 47
 
3.7%
Other values (73) 355
28.2%
ValueCountFrequency (%)
0 9
 
0.7%
1 73
5.8%
2 116
9.2%
3 146
11.6%
4 126
10.0%
5 140
11.1%
6 74
5.9%
7 63
5.0%
8 57
 
4.5%
9 47
 
3.7%
ValueCountFrequency (%)
440 1
0.1%
316 1
0.1%
290 1
0.1%
207 1
0.1%
185 1
0.1%
151 1
0.1%
130 1
0.1%
122 1
0.1%
120 1
0.1%
118 1
0.1%

생산품
Text

Distinct997
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-14T22:56:23.458730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length48
Mean length10.357653
Min length1

Characters and Unicode

Total characters13061
Distinct characters570
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique909 ?
Unique (%)72.1%

Sample

1st rowLED조명기구, 배전반, PCB시장기판 등, LED램프
2nd row육묘상자
3rd row농기계부품
4th row수전금구, 가공타일 등
5th row버섯균사체,공액리놀레산
ValueCountFrequency (%)
등 89
 
3.5%
농기계부품 83
 
3.2%
및 82
 
3.2%
부품 76
 
3.0%
자동차부품 71
 
2.8%
자동차 47
 
1.8%
농기계 25
 
1.0%
기어 24
 
0.9%
중장비 24
 
0.9%
중장비부품 21
 
0.8%
Other values (1452) 2024
78.9%
2024-03-14T22:56:25.108366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1310
 
10.0%
, 788
 
6.0%
기 583
 
4.5%
품 567
 
4.3%
부 501
 
3.8%
자 246
 
1.9%
동 231
 
1.8%
차 228
 
1.7%
계 227
 
1.7%
농 217
 
1.7%
Other values (560) 8163
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10343
79.2%
Space Separator 1310
 
10.0%
Other Punctuation 804
 
6.2%
Uppercase Letter 290
 
2.2%
Open Punctuation 137
 
1.0%
Close Punctuation 135
 
1.0%
Lowercase Letter 29
 
0.2%
Decimal Number 11
 
0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 583
 
5.6%
품 567
 
5.5%
부 501
 
4.8%
자 246
 
2.4%
동 231
 
2.2%
차 228
 
2.2%
계 227
 
2.2%
농 217
 
2.1%
장 213
 
2.1%
제 144
 
1.4%
Other values (509) 7186
69.5%
Uppercase Letter
ValueCountFrequency (%)
C 44
15.2%
T 26
 
9.0%
D 25
 
8.6%
E 23
 
7.9%
P 23
 
7.9%
L 22
 
7.6%
V 21
 
7.2%
S 17
 
5.9%
B 13
 
4.5%
U 11
 
3.8%
Other values (14) 65
22.4%
Lowercase Letter
ValueCountFrequency (%)
l 6
20.7%
i 3
10.3%
k 3
10.3%
a 3
10.3%
e 2
 
6.9%
c 2
 
6.9%
o 2
 
6.9%
p 2
 
6.9%
n 1
 
3.4%
r 1
 
3.4%
Other values (4) 4
13.8%
Decimal Number
ValueCountFrequency (%)
3 5
45.5%
2 2
 
18.2%
6 2
 
18.2%
1 1
 
9.1%
4 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 788
98.0%
. 12
 
1.5%
/ 3
 
0.4%
& 1
 
0.1%
Space Separator
ValueCountFrequency (%)
1310
100.0%
Open Punctuation
ValueCountFrequency (%)
( 137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 135
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10343
79.2%
Common 2399
 
18.4%
Latin 319
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 583
 
5.6%
품 567
 
5.5%
부 501
 
4.8%
자 246
 
2.4%
동 231
 
2.2%
차 228
 
2.2%
계 227
 
2.2%
농 217
 
2.1%
장 213
 
2.1%
제 144
 
1.4%
Other values (509) 7186
69.5%
Latin
ValueCountFrequency (%)
C 44
13.8%
T 26
 
8.2%
D 25
 
7.8%
E 23
 
7.2%
P 23
 
7.2%
L 22
 
6.9%
V 21
 
6.6%
S 17
 
5.3%
B 13
 
4.1%
U 11
 
3.4%
Other values (28) 94
29.5%
Common
ValueCountFrequency (%)
1310
54.6%
, 788
32.8%
( 137
 
5.7%
) 135
 
5.6%
. 12
 
0.5%
3 5
 
0.2%
/ 3
 
0.1%
2 2
 
0.1%
6 2
 
0.1%
- 2
 
0.1%
Other values (3) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10343
79.2%
ASCII 2718
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1310
48.2%
, 788
29.0%
( 137
 
5.0%
) 135
 
5.0%
C 44
 
1.6%
T 26
 
1.0%
D 25
 
0.9%
E 23
 
0.8%
P 23
 
0.8%
L 22
 
0.8%
Other values (41) 185
 
6.8%
Hangul
ValueCountFrequency (%)
기 583
 
5.6%
품 567
 
5.5%
부 501
 
4.8%
자 246
 
2.4%
동 231
 
2.2%
차 228
 
2.2%
계 227
 
2.2%
농 217
 
2.1%
장 213
 
2.1%
제 144
 
1.4%
Other values (509) 7186
69.5%

용지면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct939
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2921.6665
Minimum0
Maximum98502
Zeros218
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:56:25.519997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1231
median1151.96
Q33328.3
95-th percentile10594
Maximum98502
Range98502
Interquartile range (IQR)3097.3

Descriptive statistics

Standard deviation6551.33
Coefficient of variation (CV)2.2423264
Kurtosis105.81888
Mean2921.6665
Median Absolute Deviation (MAD)1151.96
Skewness8.7314015
Sum3684221.5
Variance42919925
MonotonicityNot monotonic
2024-03-14T22:56:25.785943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 218
 
17.3%
1655.0 8
 
0.6%
1653.0 7
 
0.6%
330.0 5
 
0.4%
1000.0 5
 
0.4%
991.7 4
 
0.3%
1100.0 4
 
0.3%
140.89 4
 
0.3%
826.4 3
 
0.2%
6723.0 3
 
0.2%
Other values (929) 1000
79.3%
ValueCountFrequency (%)
0.0 218
17.3%
16.5 1
 
0.1%
25.29 1
 
0.1%
29.7 1
 
0.1%
33.0 2
 
0.2%
42.9 1
 
0.1%
45.0 1
 
0.1%
46.08 1
 
0.1%
46.2 1
 
0.1%
52.92 1
 
0.1%
ValueCountFrequency (%)
98502.0 1
0.1%
97411.0 1
0.1%
84870.8 1
0.1%
76139.8 1
0.1%
55117.6 1
0.1%
41143.0 1
0.1%
33580.3 1
0.1%
31340.0 1
0.1%
27795.2 1
0.1%
26922.8 1
0.1%

제조시설면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1063
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1129.7658
Minimum6.57
Maximum69206.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-03-14T22:56:26.099093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.57
5-th percentile46.08
Q1170
median439.6
Q31057.88
95-th percentile4088.99
Maximum69206.42
Range69199.85
Interquartile range (IQR)887.88

Descriptive statistics

Standard deviation3018.5091
Coefficient of variation (CV)2.6718006
Kurtosis247.09072
Mean1129.7658
Median Absolute Deviation (MAD)329.6
Skewness13.096269
Sum1424634.7
Variance9111396.9
MonotonicityNot monotonic
2024-03-14T22:56:26.599502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 13
 
1.0%
100.0 9
 
0.7%
33.0 8
 
0.6%
660.0 8
 
0.6%
165.0 7
 
0.6%
66.0 6
 
0.5%
99.0 6
 
0.5%
132.0 6
 
0.5%
198.0 5
 
0.4%
450.0 5
 
0.4%
Other values (1053) 1188
94.2%
ValueCountFrequency (%)
6.57 1
0.1%
8.97 1
0.1%
12.0 1
0.1%
13.2 1
0.1%
14.0 1
0.1%
15.0 1
0.1%
16.5 2
0.2%
17.0 1
0.1%
17.4 1
0.1%
17.52 1
0.1%
ValueCountFrequency (%)
69206.42 1
0.1%
44964.0 1
0.1%
21958.36 1
0.1%
21776.07 1
0.1%
20877.6 1
0.1%
17995.95 1
0.1%
16530.48 1
0.1%
16206.63 1
0.1%
14771.75 1
0.1%
14463.69 1
0.1%

부대시설면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct865
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368.87821
Minimum-22
Maximum23951.22
Zeros250
Zeros (%)19.8%
Negative1
Negative (%)0.1%
Memory size11.2 KiB
2024-03-14T22:56:26.861420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-22
5-th percentile0
Q116.49
median99
Q3350.5
95-th percentile1352.76
Maximum23951.22
Range23973.22
Interquartile range (IQR)334.01

Descriptive statistics

Standard deviation1060.8514
Coefficient of variation (CV)2.8758853
Kurtosis220.23771
Mean368.87821
Median Absolute Deviation (MAD)99
Skewness12.038376
Sum465155.43
Variance1125405.8
MonotonicityNot monotonic
2024-03-14T22:56:27.114632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 250
 
19.8%
33.0 7
 
0.6%
66.0 7
 
0.6%
10.0 6
 
0.5%
150.0 6
 
0.5%
40.0 6
 
0.5%
26.4 6
 
0.5%
99.0 6
 
0.5%
200.0 5
 
0.4%
64.24 5
 
0.4%
Other values (855) 957
75.9%
ValueCountFrequency (%)
-22.0 1
 
0.1%
0.0 250
19.8%
1.13 1
 
0.1%
2.9 1
 
0.1%
3.2 2
 
0.2%
3.24 1
 
0.1%
3.6 1
 
0.1%
3.64 1
 
0.1%
3.78 1
 
0.1%
4.35 1
 
0.1%
ValueCountFrequency (%)
23951.22 1
0.1%
13445.13 1
0.1%
8898.79 1
0.1%
7389.04 1
0.1%
6800.84 1
0.1%
6750.95 1
0.1%
5881.84 1
0.1%
5801.68 1
0.1%
5390.36 1
0.1%
5036.46 1
0.1%

Interactions

2024-03-14T22:55:55.796931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:39.783926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:42.208946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-14T22:55:46.384785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:47.861689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:49.301683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:51.089492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:53.364566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-14T22:55:42.484160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-14T22:55:46.541308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:48.012082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:49.690394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:51.260207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:53.640685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:56.344227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-14T22:55:42.770380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:45.151756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-14T22:55:49.918648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:51.540595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:53.925263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:56.614513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:40.618352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:43.052180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:45.326410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:46.861424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:48.326465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:50.084803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-14T22:55:54.206294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:56.865670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:40.868528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:43.302124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:45.482169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:47.006290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:48.491460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:50.236022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:52.063173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:54.463524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:57.107791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:41.115757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:43.552084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:45.633301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:47.148122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:48.694014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:50.384875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:52.314530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:54.721185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:57.358847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:41.373017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:43.815710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:45.791394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:47.355237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:48.847014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:50.530370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:52.566321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:54.979428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:57.613158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:41.668065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:44.089661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:45.955955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:47.537872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:48.994555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:50.682619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:52.829977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:55.247576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:57.887947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:41.941810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:44.371784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:46.210217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:47.711458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:49.154333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:50.875511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:53.103558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:55:55.523961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:56:27.286973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번단지명남종업원여종업원외국인(남)외국인(여)종업원수용지면적제조시설면적부대시설면적
순번1.0000.1900.0000.0000.0000.0840.0270.1070.0260.061
단지명0.1901.0000.3210.2700.4720.3840.2660.5470.1700.565
남종업원0.0000.3211.0000.6270.5230.2750.9540.7620.7730.757
여종업원0.0000.2700.6271.0000.3930.4860.7140.5050.4690.529
외국인(남)0.0000.4720.5230.3931.0000.5070.6050.2370.4410.535
외국인(여)0.0840.3840.2750.4860.5071.0000.2920.0000.0670.239
종업원수0.0270.2660.9540.7140.6050.2921.0000.7390.7980.785
용지면적0.1070.5470.7620.5050.2370.0000.7391.0000.9190.902
제조시설면적0.0260.1700.7730.4690.4410.0670.7980.9191.0000.935
부대시설면적0.0610.5650.7570.5290.5350.2390.7850.9020.9351.000
2024-03-14T22:56:27.553411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번남종업원여종업원외국인(남)외국인(여)종업원수용지면적제조시설면적부대시설면적단지명
순번1.000-0.182-0.056-0.075-0.045-0.162-0.119-0.094-0.1180.082
남종업원-0.1821.0000.4000.2340.0880.8990.5770.5950.5120.170
여종업원-0.0560.4001.0000.2210.1880.6340.2850.3520.3160.130
외국인(남)-0.0750.2340.2211.0000.5640.4170.3020.4350.2960.161
외국인(여)-0.0450.0880.1880.5641.0000.2090.1540.2320.1830.193
종업원수-0.1620.8990.6340.4170.2091.0000.5970.6400.5530.134
용지면적-0.1190.5770.2850.3020.1540.5971.0000.7990.7010.295
제조시설면적-0.0940.5950.3520.4350.2320.6400.7991.0000.6280.087
부대시설면적-0.1180.5120.3160.2960.1830.5530.7010.6281.0000.331
단지명0.0820.1700.1300.1610.1930.1340.2950.0870.3311.000

Missing values

2024-03-14T22:55:58.281500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:55:58.964368image/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.
2024-03-14T22:55:59.426258image/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

순번단지명회사명공장대표주소(도로명)공장대표주소(지번)업종번호업종명전화번호팩스번호남종업원여종업원외국인(남)외국인(여)종업원수생산품용지면적제조시설면적부대시설면적
01진주일반산업단지(유)경남라이팅경상남도 진주시 사봉면 산업단지로 102경상남도 진주시 사봉면 사곡리 1850-226299, 25111, 25112, 25113, 25114, 25119, 25999, 26295, 26410, 26421, 28111, 28123, 28410, 28422, 28423, 28429, 28903그 외 기타 전자부품 제조업 외 16 종055-795-6685055-276-66867<NA><NA><NA>7LED조명기구, 배전반, PCB시장기판 등, LED램프860.86786.1474.72
12진주상평일반산업단지(유)동양프라스틱경상남도 진주시 동진로264번길 12 (상대동) 외 1필지경상남도 진주시 상대2동 313-2번지 외 1필지22232,포장용 플라스틱 성형용기 제조업055-762-4588055-757-1438850013육묘상자1858.2645.410.0
23진주상평일반산업단지(유)유창이엔지경상남도 진주시 돗골로58번길 19 (상평동)경상남도 진주시 상평동 201-4번지29210,농업 및 임업용 기계 제조업055-753-9396055-753-103631004농기계부품1322.3629.0171.64
34진주상평일반산업단지(유)화신테크경상남도 진주시 남강로 1273 (상평동)경상남도 진주시 상평동 55-39번지23222, 23232위생용 및 산업용 도자기 제조업 외 1 종055-755-6811055-755-2288155<NA><NA>20수전금구, 가공타일 등15724.07180.981779.0
45진주생물산업전문농공단지(주)HK바이오텍경상남도 진주시 문산읍 월아산로950번길 6경상남도 진주시 문산읍 이곡리 1182번지10797, 10403건강기능식품 제조업 외 1 종055-762-9307055-762-94075130018버섯균사체,공액리놀레산5550.41284.781237.03
56진주정촌일반산업단지(주)가야데이터경상남도 진주시 정촌면 연꽃로165번길 5경상남도 진주시 정촌면 예하리 1246-14번지26310, 26321컴퓨터 제조업 외 1 종055-790-959802-780-488072009반도체메모리스토리지시스템3536.5277.02338.58
67진주정촌일반산업단지(주)감로경상남도 진주시 정촌면 연꽃로 81경상남도 진주시 정촌면 예하리 1250-3번지25122, 24132금속탱크 및 저장용기 제조업 외 1 종055-741-2336055-741-232632<NA><NA>5스테인리스 물탱크, 스테인리스 파이프0.01136.0360.0
78진주상평일반산업단지(주)경남철공경상남도 진주시 공단로 80 (상평동)경상남도 진주시 상평동 222-10번지25112,구조용 금속 판제품 및 공작물 제조업055-752-3205055-758-320571008철구조물1818.2242.022.0
89진주정촌일반산업단지(주)경남특장차경상남도 진주시 정촌면 연꽃로165번길 7경상남도 진주시 정촌면 예하리 1246-13번지30201, 30202차체 및 특장차 제조업 외 1 종055-757-1683055-759-05721730020특장차3270.01396.34581.28
910<NA>(주)경민경상남도 진주시 미천면 진산로2018번길 50경상남도 진주시 미천면 효자리 188번지13225,직물포대 제조업055-744-2920055-744-2928433010pp마대10467.01818.8590.0
순번단지명회사명공장대표주소(도로명)공장대표주소(지번)업종번호업종명전화번호팩스번호남종업원여종업원외국인(남)외국인(여)종업원수생산품용지면적제조시설면적부대시설면적
12511252<NA>흥진ENG경상남도 진주시 남강로1179번길 10-4 (상평동)경상남도 진주시 상평동 302-4번지29294,주형 및 금형 제조업055-752-8614055-753-600940004주형(금형)264.1189.4780.59
12521253<NA>희석정밀공업경상남도 진주시 명석면 광제산로610번길 11경상남도 진주시 명석면 계원리 285-4번지25924,절삭가공 및 유사처리업055-745-3610055-745-3810212<NA>5자동차부품1091.0150.0150.0
12531254진주정촌일반산업단지훈레이저경상남도 진주시 정촌면 산업로39번길 25경상남도 진주시 정촌면 예하리 1249-2번지29210,농업 및 임업용 기계 제조업<NA>055-763-471242<NA><NA>6농기계부품1929.7807.41519.87
12541255<NA>휴림디자인경상남도 진주시 문산읍 소문길17번길 7-14경상남도 진주시 문산읍 소문리 220-125932, 32029, 32091일반철물 제조업 외 2 종ARS-1599-9579055-754-01193<NA><NA><NA>3흔들의자, 옥외벤치, 옥외테이블, 가드레일발판0.0167.50.0
12551256<NA>휴먼바이오텍(주)경상남도 진주시 문산읍 월아산로 991, 성장지원동 307호 (진주바이오산업진흥원)경상남도 진주시 문산읍 삼곡리 1033번지 진주바이오산업진흥원 성장지원동 307호21102,생물학적 제제 제조업055-763-6127055-763-6131<NA>4<NA><NA>4화장품0.0132.066.0
12561257진주상평일반산업단지흥성공업(주)경상남도 진주시 남강로1367번길 5 (상대동)경상남도 진주시 상대동 33-47번지30320,자동차 차체용 신품 부품 제조업055-762-0674055-762-06471010011자동차 차체부품1655.0336.093.75
12571258진주뿌리일반산업단지흥성공업(주)경상남도 진주시 정촌면 뿌리산단로50번길 12경상남도 진주시 정촌면 예하리 1386-230320,자동차 차체용 신품 부품 제조업055-762-0674055-762-0647101<NA><NA>11트레일러 및 중장비 부품4689.31904.94719.46
12581259진주상평일반산업단지흥일기계경상남도 진주시 동진로311번길 12 (상대동)경상남도 진주시 상대동 33-41번지29142,기어 및 동력전달장치 제조업055-753-3506055-759-857132005기어1330.0539.97209.83
12591260<NA>흥진ENG경상남도 진주시 남강로1179번길 10-4 (상평동)경상남도 진주시 상평동 302-4번지29294,주형 및 금형 제조업055-752-8614055-753-600940004주형(금형)264.1189.4780.59
12601261<NA>희석정밀공업경상남도 진주시 명석면 광제산로610번길 11경상남도 진주시 명석면 계원리 285-4번지25924,절삭가공 및 유사처리업055-745-3610055-745-3810212<NA>5자동차부품1091.0150.0150.0