Overview

Dataset statistics

Number of variables18
Number of observations1261
Missing cells1794
Missing cells (%)7.9%
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.
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15034937

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 96 (7.6%) missing valuesMissing
팩스번호 has 92 (7.3%) missing valuesMissing
남종업원 has 25 (2.0%) missing valuesMissing
여종업원 has 213 (16.9%) missing valuesMissing
외국인(남) has 645 (51.1%) missing valuesMissing
외국인(여) has 716 (56.8%) missing valuesMissing
순번 has unique valuesUnique
여종업원 has 122 (9.7%) zerosZeros
외국인(남) has 433 (34.3%) zerosZeros
외국인(여) has 493 (39.1%) zerosZeros
용지면적 has 218 (17.3%) zerosZeros
부대시설면적 has 257 (20.4%) zerosZeros

Reproduction

Analysis started2023-12-10 23:20:47.405382
Analysis finished2023-12-10 23:20:56.538054
Duration9.13 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
2023-12-11T08:20:56.597884image/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
2023-12-11T08:20:56.713811image/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>
513 
진주상평일반산업단지
434 
진주정촌일반산업단지
96 
진주일반산업단지
68 
진주뿌리일반산업단지
 
38
Other values (7)
112 

Length

Max length12
Median length10
Mean length7.3996828
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 513
40.7%
진주상평일반산업단지 434
34.4%
진주정촌일반산업단지 96
 
7.6%
진주일반산업단지 68
 
5.4%
진주뿌리일반산업단지 38
 
3.0%
진주실크전문농공단지 31
 
2.5%
진주생물산업전문농공단지 21
 
1.7%
진주사봉농공단지 18
 
1.4%
진주진성농공단지 16
 
1.3%
진주대곡농공단지 15
 
1.2%
Other values (2) 11
 
0.9%

Length

2023-12-11T08:20:56.839864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 513
40.7%
진주상평일반산업단지 434
34.4%
진주정촌일반산업단지 96
 
7.6%
진주일반산업단지 68
 
5.4%
진주뿌리일반산업단지 38
 
3.0%
진주실크전문농공단지 31
 
2.5%
진주생물산업전문농공단지 21
 
1.7%
진주사봉농공단지 18
 
1.4%
진주진성농공단지 16
 
1.3%
진주대곡농공단지 15
 
1.2%
Other values (2) 11
 
0.9%

회사명
Text

Distinct1220
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2023-12-11T08:20:57.057173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length6.6090404
Min length1

Characters and Unicode

Total characters8334
Distinct characters453
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

Unique1180 ?
Unique (%)93.6%

Sample

1st row(유)경남라이팅
2nd row(유)동양프라스틱
3rd row(유)유창이엔지
4th row(유)화신테크
5th row(주)HK바이오텍
ValueCountFrequency (%)
주식회사 85
 
5.9%
농업회사법인 16
 
1.1%
제2공장 9
 
0.6%
2공장 7
 
0.5%
진주공장 5
 
0.3%
3공장 4
 
0.3%
사봉공장 4
 
0.3%
주)명림 3
 
0.2%
영농조합법인 3
 
0.2%
주)성광 3
 
0.2%
Other values (1226) 1300
90.3%
2023-12-11T08:20:57.390235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 637
 
7.6%
( 475
 
5.7%
) 475
 
5.7%
업 298
 
3.6%
공 265
 
3.2%
사 244
 
2.9%
이 189
 
2.3%
정 181
 
2.2%
179
 
2.1%
진 150
 
1.8%
Other values (443) 5241
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7032
84.4%
Open Punctuation 475
 
5.7%
Close Punctuation 475
 
5.7%
Space Separator 179
 
2.1%
Uppercase Letter 96
 
1.2%
Decimal Number 51
 
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 637
 
9.1%
업 298
 
4.2%
공 265
 
3.8%
사 244
 
3.5%
이 189
 
2.7%
정 181
 
2.6%
진 150
 
2.1%
산 143
 
2.0%
성 128
 
1.8%
회 128
 
1.8%
Other values (400) 4669
66.4%
Uppercase Letter
ValueCountFrequency (%)
E 13
13.5%
G 12
12.5%
N 11
11.5%
S 9
9.4%
T 7
 
7.3%
M 6
 
6.2%
C 6
 
6.2%
D 6
 
6.2%
P 4
 
4.2%
K 4
 
4.2%
Other values (11) 18
18.8%
Lowercase Letter
ValueCountFrequency (%)
o 2
22.2%
h 1
11.1%
c 1
11.1%
e 1
11.1%
t 1
11.1%
l 1
11.1%
u 1
11.1%
g 1
11.1%
Decimal Number
ValueCountFrequency (%)
2 31
60.8%
3 11
 
21.6%
0 3
 
5.9%
4 2
 
3.9%
1 2
 
3.9%
8 2
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 9
75.0%
& 2
 
16.7%
, 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 475
100.0%
Close Punctuation
ValueCountFrequency (%)
) 475
100.0%
Space Separator
ValueCountFrequency (%)
179
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7036
84.4%
Common 1193
 
14.3%
Latin 105
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 637
 
9.1%
업 298
 
4.2%
공 265
 
3.8%
사 244
 
3.5%
이 189
 
2.7%
정 181
 
2.6%
진 150
 
2.1%
산 143
 
2.0%
성 128
 
1.8%
회 128
 
1.8%
Other values (401) 4673
66.4%
Latin
ValueCountFrequency (%)
E 13
12.4%
G 12
11.4%
N 11
10.5%
S 9
 
8.6%
T 7
 
6.7%
M 6
 
5.7%
C 6
 
5.7%
D 6
 
5.7%
P 4
 
3.8%
K 4
 
3.8%
Other values (19) 27
25.7%
Common
ValueCountFrequency (%)
( 475
39.8%
) 475
39.8%
179
 
15.0%
2 31
 
2.6%
3 11
 
0.9%
. 9
 
0.8%
0 3
 
0.3%
4 2
 
0.2%
1 2
 
0.2%
& 2
 
0.2%
Other values (3) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7032
84.4%
ASCII 1298
 
15.6%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 637
 
9.1%
업 298
 
4.2%
공 265
 
3.8%
사 244
 
3.5%
이 189
 
2.7%
정 181
 
2.6%
진 150
 
2.1%
산 143
 
2.0%
성 128
 
1.8%
회 128
 
1.8%
Other values (400) 4669
66.4%
ASCII
ValueCountFrequency (%)
( 475
36.6%
) 475
36.6%
179
 
13.8%
2 31
 
2.4%
E 13
 
1.0%
G 12
 
0.9%
3 11
 
0.8%
N 11
 
0.8%
S 9
 
0.7%
. 9
 
0.7%
Other values (32) 73
 
5.6%
None
ValueCountFrequency (%)
㈜ 4
100.0%

공장대표주소(도로명)
Text

Distinct1104
Distinct (%)88.0%
Missing7
Missing (%)0.6%
Memory size10.0 KiB
2023-12-11T08:20:57.623150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length80
Median length59
Mean length27.356459
Min length11

Characters and Unicode

Total characters34305
Distinct characters228
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

Unique987 ?
Unique (%)78.7%

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 1253
 
17.6%
진주시 1253
 
17.6%
상평동 365
 
5.1%
정촌면 139
 
1.9%
상대동 123
 
1.7%
외 122
 
1.7%
문산읍 117
 
1.6%
사봉면 104
 
1.5%
1필지 79
 
1.1%
12 66
 
0.9%
Other values (1021) 3513
49.2%
2023-12-11T08:20:57.994477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5881
 
17.1%
상 1802
 
5.3%
진 1509
 
4.4%
1 1484
 
4.3%
남 1370
 
4.0%
주 1326
 
3.9%
도 1310
 
3.8%
경 1302
 
3.8%
시 1256
 
3.7%
로 1178
 
3.4%
Other values (218) 15887
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20226
59.0%
Decimal Number 6068
 
17.7%
Space Separator 5881
 
17.1%
Open Punctuation 774
 
2.3%
Close Punctuation 767
 
2.2%
Dash Punctuation 261
 
0.8%
Other Punctuation 207
 
0.6%
Uppercase Letter 121
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1802
 
8.9%
진 1509
 
7.5%
남 1370
 
6.8%
주 1326
 
6.6%
도 1310
 
6.5%
경 1302
 
6.4%
시 1256
 
6.2%
로 1178
 
5.8%
동 1072
 
5.3%
길 755
 
3.7%
Other values (194) 7346
36.3%
Decimal Number
ValueCountFrequency (%)
1 1484
24.5%
2 753
12.4%
4 567
 
9.3%
5 548
 
9.0%
3 536
 
8.8%
9 505
 
8.3%
0 503
 
8.3%
6 435
 
7.2%
7 382
 
6.3%
8 355
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 56
46.3%
A 53
43.8%
C 6
 
5.0%
S 2
 
1.7%
I 1
 
0.8%
T 1
 
0.8%
K 1
 
0.8%
D 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 206
99.5%
& 1
 
0.5%
Space Separator
ValueCountFrequency (%)
5881
100.0%
Open Punctuation
ValueCountFrequency (%)
( 774
100.0%
Close Punctuation
ValueCountFrequency (%)
) 767
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20226
59.0%
Common 13958
40.7%
Latin 121
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1802
 
8.9%
진 1509
 
7.5%
남 1370
 
6.8%
주 1326
 
6.6%
도 1310
 
6.5%
경 1302
 
6.4%
시 1256
 
6.2%
로 1178
 
5.8%
동 1072
 
5.3%
길 755
 
3.7%
Other values (194) 7346
36.3%
Common
ValueCountFrequency (%)
5881
42.1%
1 1484
 
10.6%
( 774
 
5.5%
) 767
 
5.5%
2 753
 
5.4%
4 567
 
4.1%
5 548
 
3.9%
3 536
 
3.8%
9 505
 
3.6%
0 503
 
3.6%
Other values (6) 1640
 
11.7%
Latin
ValueCountFrequency (%)
B 56
46.3%
A 53
43.8%
C 6
 
5.0%
S 2
 
1.7%
I 1
 
0.8%
T 1
 
0.8%
K 1
 
0.8%
D 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20226
59.0%
ASCII 14079
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5881
41.8%
1 1484
 
10.5%
( 774
 
5.5%
) 767
 
5.4%
2 753
 
5.3%
4 567
 
4.0%
5 548
 
3.9%
3 536
 
3.8%
9 505
 
3.6%
0 503
 
3.6%
Other values (14) 1761
 
12.5%
Hangul
ValueCountFrequency (%)
상 1802
 
8.9%
진 1509
 
7.5%
남 1370
 
6.8%
주 1326
 
6.6%
도 1310
 
6.5%
경 1302
 
6.4%
시 1256
 
6.2%
로 1178
 
5.8%
동 1072
 
5.3%
길 755
 
3.7%
Other values (194) 7346
36.3%

공장대표주소(지번)
Text

Distinct1140
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2023-12-11T08:20:58.248101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length56
Mean length24.699445
Min length13

Characters and Unicode

Total characters31146
Distinct characters207
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

Unique1045 ?
Unique (%)82.9%

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
19.4%
진주시 1260
19.4%
상평동 388
 
6.0%
정촌면 143
 
2.2%
상대동 135
 
2.1%
예하리 131
 
2.0%
외 124
 
1.9%
문산읍 116
 
1.8%
사봉면 105
 
1.6%
1필지 81
 
1.2%
Other values (1338) 2763
42.5%
2023-12-11T08:20:58.625714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5294
 
17.0%
상 1864
 
6.0%
1 1434
 
4.6%
진 1378
 
4.4%
주 1296
 
4.2%
경 1294
 
4.2%
남 1272
 
4.1%
시 1263
 
4.1%
도 1260
 
4.0%
지 1159
 
3.7%
Other values (197) 13632
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18331
58.9%
Decimal Number 6220
 
20.0%
Space Separator 5294
 
17.0%
Dash Punctuation 1091
 
3.5%
Uppercase Letter 117
 
0.4%
Open Punctuation 42
 
0.1%
Close Punctuation 35
 
0.1%
Other Punctuation 16
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1864
 
10.2%
진 1378
 
7.5%
주 1296
 
7.1%
경 1294
 
7.1%
남 1272
 
6.9%
시 1263
 
6.9%
도 1260
 
6.9%
지 1159
 
6.3%
번 972
 
5.3%
동 823
 
4.5%
Other values (174) 5750
31.4%
Decimal Number
ValueCountFrequency (%)
1 1434
23.1%
2 940
15.1%
3 873
14.0%
5 575
9.2%
0 557
 
9.0%
4 479
 
7.7%
6 450
 
7.2%
8 317
 
5.1%
7 300
 
4.8%
9 295
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 54
46.2%
A 53
45.3%
C 5
 
4.3%
S 2
 
1.7%
D 1
 
0.9%
I 1
 
0.9%
T 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
& 1
 
6.2%
Space Separator
ValueCountFrequency (%)
5294
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1091
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18331
58.9%
Common 12698
40.8%
Latin 117
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1864
 
10.2%
진 1378
 
7.5%
주 1296
 
7.1%
경 1294
 
7.1%
남 1272
 
6.9%
시 1263
 
6.9%
도 1260
 
6.9%
지 1159
 
6.3%
번 972
 
5.3%
동 823
 
4.5%
Other values (174) 5750
31.4%
Common
ValueCountFrequency (%)
5294
41.7%
1 1434
 
11.3%
- 1091
 
8.6%
2 940
 
7.4%
3 873
 
6.9%
5 575
 
4.5%
0 557
 
4.4%
4 479
 
3.8%
6 450
 
3.5%
8 317
 
2.5%
Other values (6) 688
 
5.4%
Latin
ValueCountFrequency (%)
B 54
46.2%
A 53
45.3%
C 5
 
4.3%
S 2
 
1.7%
D 1
 
0.9%
I 1
 
0.9%
T 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18331
58.9%
ASCII 12815
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5294
41.3%
1 1434
 
11.2%
- 1091
 
8.5%
2 940
 
7.3%
3 873
 
6.8%
5 575
 
4.5%
0 557
 
4.3%
4 479
 
3.7%
6 450
 
3.5%
8 317
 
2.5%
Other values (13) 805
 
6.3%
Hangul
ValueCountFrequency (%)
상 1864
 
10.2%
진 1378
 
7.5%
주 1296
 
7.1%
경 1294
 
7.1%
남 1272
 
6.9%
시 1263
 
6.9%
도 1260
 
6.9%
지 1159
 
6.3%
번 972
 
5.3%
동 823
 
4.5%
Other values (174) 5750
31.4%

업종번호
Text

Distinct519
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2023-12-11T08:20:58.912086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length250
Median length7
Mean length12.668517
Min length7

Characters and Unicode

Total characters15975
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

Unique373 ?
Unique (%)29.6%

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 206
 
8.5%
29210 137
 
5.7%
30400 71
 
2.9%
29142 64
 
2.7%
30399 59
 
2.4%
25113 57
 
2.4%
25112 57
 
2.4%
30391 49
 
2.0%
30392 47
 
1.9%
31322 40
 
1.7%
Other values (306) 1624
67.4%
2023-12-11T08:20:59.562135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3342
20.9%
1 2318
14.5%
, 1960
12.3%
1960
12.3%
9 1700
10.6%
3 1452
9.1%
0 1136
 
7.1%
4 798
 
5.0%
5 716
 
4.5%
6 221
 
1.4%
Other values (2) 372
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12055
75.5%
Other Punctuation 1960
 
12.3%
Space Separator 1960
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3342
27.7%
1 2318
19.2%
9 1700
14.1%
3 1452
12.0%
0 1136
 
9.4%
4 798
 
6.6%
5 716
 
5.9%
6 221
 
1.8%
8 205
 
1.7%
7 167
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 1960
100.0%
Space Separator
ValueCountFrequency (%)
1960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15975
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3342
20.9%
1 2318
14.5%
, 1960
12.3%
1960
12.3%
9 1700
10.6%
3 1452
9.1%
0 1136
 
7.1%
4 798
 
5.0%
5 716
 
4.5%
6 221
 
1.4%
Other values (2) 372
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3342
20.9%
1 2318
14.5%
, 1960
12.3%
1960
12.3%
9 1700
10.6%
3 1452
9.1%
0 1136
 
7.1%
4 798
 
5.0%
5 716
 
4.5%
6 221
 
1.4%
Other values (2) 372
 
2.3%

업종명
Text

Distinct427
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2023-12-11T08:20:59.849015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length29
Mean length16.458366
Min length3

Characters and Unicode

Total characters20754
Distinct characters281
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

Unique250 ?
Unique (%)19.8%

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 930
 
14.0%
및 731
 
11.0%
외 552
 
8.3%
종 451
 
6.8%
1 228
 
3.4%
기타 205
 
3.1%
절삭가공 186
 
2.8%
유사처리업 186
 
2.8%
기계 127
 
1.9%
금속 123
 
1.8%
Other values (421) 2940
44.2%
2023-12-11T08:21:00.285225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5399
26.0%
업 1524
 
7.3%
제 1155
 
5.6%
조 1123
 
5.4%
및 731
 
3.5%
기 614
 
3.0%
외 562
 
2.7%
종 467
 
2.3%
용 409
 
2.0%
품 373
 
1.8%
Other values (271) 8397
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14770
71.2%
Space Separator 5399
 
26.0%
Decimal Number 470
 
2.3%
Other Punctuation 99
 
0.5%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 1524
 
10.3%
제 1155
 
7.8%
조 1123
 
7.6%
및 731
 
4.9%
기 614
 
4.2%
외 562
 
3.8%
종 467
 
3.2%
용 409
 
2.8%
품 373
 
2.5%
공 313
 
2.1%
Other values (257) 7499
50.8%
Decimal Number
ValueCountFrequency (%)
1 247
52.6%
3 70
 
14.9%
2 69
 
14.7%
4 36
 
7.7%
5 20
 
4.3%
6 12
 
2.6%
7 10
 
2.1%
9 3
 
0.6%
8 3
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 92
92.9%
. 7
 
7.1%
Space Separator
ValueCountFrequency (%)
5399
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14770
71.2%
Common 5984
28.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 1524
 
10.3%
제 1155
 
7.8%
조 1123
 
7.6%
및 731
 
4.9%
기 614
 
4.2%
외 562
 
3.8%
종 467
 
3.2%
용 409
 
2.8%
품 373
 
2.5%
공 313
 
2.1%
Other values (257) 7499
50.8%
Common
ValueCountFrequency (%)
5399
90.2%
1 247
 
4.1%
, 92
 
1.5%
3 70
 
1.2%
2 69
 
1.2%
4 36
 
0.6%
5 20
 
0.3%
6 12
 
0.2%
7 10
 
0.2%
( 8
 
0.1%
Other values (4) 21
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14762
71.1%
ASCII 5984
28.8%
Compat Jamo 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5399
90.2%
1 247
 
4.1%
, 92
 
1.5%
3 70
 
1.2%
2 69
 
1.2%
4 36
 
0.6%
5 20
 
0.3%
6 12
 
0.2%
7 10
 
0.2%
( 8
 
0.1%
Other values (4) 21
 
0.4%
Hangul
ValueCountFrequency (%)
업 1524
 
10.3%
제 1155
 
7.8%
조 1123
 
7.6%
및 731
 
5.0%
기 614
 
4.2%
외 562
 
3.8%
종 467
 
3.2%
용 409
 
2.8%
품 373
 
2.5%
공 313
 
2.1%
Other values (256) 7491
50.7%
Compat Jamo
ValueCountFrequency (%)
ㆍ 8
100.0%

전화번호
Text

MISSING 

Distinct1055
Distinct (%)90.6%
Missing96
Missing (%)7.6%
Memory size10.0 KiB
2023-12-11T08:21:00.495975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.03176
Min length12

Characters and Unicode

Total characters14017
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

Unique958 ?
Unique (%)82.2%

Sample

1st row055-795-6685
2nd row055-762-4588
3rd row055-753-9396
4th row055-755-6811
5th row055-762-9307
ValueCountFrequency (%)
055-762-5200 4
 
0.3%
055-749-3200 3
 
0.3%
055-746-6483 3
 
0.3%
055-752-2198 3
 
0.3%
055-758-4200 3
 
0.3%
055-758-7377 3
 
0.3%
055-758-1546 3
 
0.3%
055-744-2155 3
 
0.3%
055-757-2541 3
 
0.3%
055-759-6161 3
 
0.3%
Other values (1045) 1134
97.3%
2023-12-11T08:21:00.823035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3514
25.1%
- 2330
16.6%
0 1905
13.6%
7 1703
12.1%
2 742
 
5.3%
6 730
 
5.2%
1 662
 
4.7%
8 647
 
4.6%
3 625
 
4.5%
4 621
 
4.4%
Other values (4) 538
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11672
83.3%
Dash Punctuation 2330
 
16.6%
Uppercase Letter 15
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3514
30.1%
0 1905
16.3%
7 1703
14.6%
2 742
 
6.4%
6 730
 
6.3%
1 662
 
5.7%
8 647
 
5.5%
3 625
 
5.4%
4 621
 
5.3%
9 523
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
A 5
33.3%
R 5
33.3%
S 5
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14002
99.9%
Latin 15
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3514
25.1%
- 2330
16.6%
0 1905
13.6%
7 1703
12.2%
2 742
 
5.3%
6 730
 
5.2%
1 662
 
4.7%
8 647
 
4.6%
3 625
 
4.5%
4 621
 
4.4%
Latin
ValueCountFrequency (%)
A 5
33.3%
R 5
33.3%
S 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3514
25.1%
- 2330
16.6%
0 1905
13.6%
7 1703
12.1%
2 742
 
5.3%
6 730
 
5.2%
1 662
 
4.7%
8 647
 
4.6%
3 625
 
4.5%
4 621
 
4.4%
Other values (4) 538
 
3.8%

팩스번호
Text

MISSING 

Distinct1002
Distinct (%)85.7%
Missing92
Missing (%)7.3%
Memory size10.0 KiB
2023-12-11T08:21:01.003172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.789564
Min length2

Characters and Unicode

Total characters13782
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

Unique895 ?
Unique (%)76.6%

Sample

1st row055-276-6686
2nd row055-757-1438
3rd row055-753-1036
4th row055-755-2288
5th row055-762-9407
ValueCountFrequency (%)
55 30
 
2.6%
055-741-2326 6
 
0.5%
055-755-1443 5
 
0.4%
055-758-5682 4
 
0.3%
055-757-4622 4
 
0.3%
055-744-2158 4
 
0.3%
055-795-1777 4
 
0.3%
055-758-5583 3
 
0.3%
055-752-8766 3
 
0.3%
055-762-8529 3
 
0.3%
Other values (992) 1103
94.4%
2023-12-11T08:21:01.308280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3413
24.8%
- 2270
16.5%
0 1787
13.0%
7 1614
11.7%
6 768
 
5.6%
2 758
 
5.5%
4 667
 
4.8%
8 664
 
4.8%
3 659
 
4.8%
1 624
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11512
83.5%
Dash Punctuation 2270
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3413
29.6%
0 1787
15.5%
7 1614
14.0%
6 768
 
6.7%
2 758
 
6.6%
4 667
 
5.8%
8 664
 
5.8%
3 659
 
5.7%
1 624
 
5.4%
9 558
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 2270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13782
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3413
24.8%
- 2270
16.5%
0 1787
13.0%
7 1614
11.7%
6 768
 
5.6%
2 758
 
5.5%
4 667
 
4.8%
8 664
 
4.8%
3 659
 
4.8%
1 624
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3413
24.8%
- 2270
16.5%
0 1787
13.0%
7 1614
11.7%
6 768
 
5.6%
2 758
 
5.5%
4 667
 
4.8%
8 664
 
4.8%
3 659
 
4.8%
1 624
 
4.5%

남종업원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct68
Distinct (%)5.5%
Missing25
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean8.8899676
Minimum0
Maximum400
Zeros8
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:01.436873image/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 deviation20.230154
Coefficient of variation (CV)2.2756162
Kurtosis166.26622
Mean8.8899676
Median Absolute Deviation (MAD)2
Skewness10.784895
Sum10988
Variance409.25914
MonotonicityNot monotonic
2023-12-11T08:21:01.559180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 185
14.7%
2 183
14.5%
3 158
12.5%
4 125
9.9%
5 101
8.0%
7 71
 
5.6%
6 60
 
4.8%
8 48
 
3.8%
10 34
 
2.7%
9 33
 
2.6%
Other values (58) 238
18.9%
(Missing) 25
 
2.0%
ValueCountFrequency (%)
0 8
 
0.6%
1 185
14.7%
2 183
14.5%
3 158
12.5%
4 125
9.9%
5 101
8.0%
6 60
 
4.8%
7 71
 
5.6%
8 48
 
3.8%
9 33
 
2.6%
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 (%)3.1%
Missing213
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean3.226145
Minimum0
Maximum73
Zeros122
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:01.668133image/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 deviation5.9713902
Coefficient of variation (CV)1.8509367
Kurtosis59.590062
Mean3.226145
Median Absolute Deviation (MAD)1
Skewness6.6360402
Sum3381
Variance35.657501
MonotonicityNot monotonic
2023-12-11T08:21:01.773295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 373
29.6%
2 206
16.3%
0 122
 
9.7%
3 113
 
9.0%
4 52
 
4.1%
5 50
 
4.0%
6 24
 
1.9%
10 17
 
1.3%
8 16
 
1.3%
7 15
 
1.2%
Other values (23) 60
 
4.8%
(Missing) 213
16.9%
ValueCountFrequency (%)
0 122
 
9.7%
1 373
29.6%
2 206
16.3%
3 113
 
9.0%
4 52
 
4.1%
5 50
 
4.0%
6 24
 
1.9%
7 15
 
1.2%
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%
Missing645
Missing (%)51.1%
Infinite0
Infinite (%)0.0%
Mean1.5211039
Minimum0
Maximum21
Zeros433
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:01.879060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.1888336
Coefficient of variation (CV)2.0963943
Kurtosis8.7856713
Mean1.5211039
Median Absolute Deviation (MAD)0
Skewness2.7584828
Sum937
Variance10.16866
MonotonicityNot monotonic
2023-12-11T08:21:01.965817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 433
34.3%
1 33
 
2.6%
3 28
 
2.2%
4 26
 
2.1%
2 16
 
1.3%
5 16
 
1.3%
6 14
 
1.1%
7 10
 
0.8%
10 9
 
0.7%
8 8
 
0.6%
Other values (9) 23
 
1.8%
(Missing) 645
51.1%
ValueCountFrequency (%)
0 433
34.3%
1 33
 
2.6%
2 16
 
1.3%
3 28
 
2.2%
4 26
 
2.1%
5 16
 
1.3%
6 14
 
1.1%
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 9
0.7%
9 7
0.6%

외국인(여)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)1.5%
Missing716
Missing (%)56.8%
Infinite0
Infinite (%)0.0%
Mean0.17247706
Minimum0
Maximum9
Zeros493
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:02.049765image/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 deviation0.72733796
Coefficient of variation (CV)4.2170126
Kurtosis70.61435
Mean0.17247706
Median Absolute Deviation (MAD)0
Skewness7.3473546
Sum94
Variance0.52902051
MonotonicityNot monotonic
2023-12-11T08:21:02.126913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 493
39.1%
1 32
 
2.5%
2 13
 
1.0%
3 2
 
0.2%
4 2
 
0.2%
8 1
 
0.1%
5 1
 
0.1%
9 1
 
0.1%
(Missing) 716
56.8%
ValueCountFrequency (%)
0 493
39.1%
1 32
 
2.5%
2 13
 
1.0%
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 13
 
1.0%
1 32
 
2.5%
0 493
39.1%

종업원수
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.21253
Minimum0
Maximum440
Zeros10
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:02.236192image/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 deviation24.162135
Coefficient of variation (CV)1.9784709
Kurtosis118.81289
Mean12.21253
Median Absolute Deviation (MAD)3
Skewness8.9585066
Sum15400
Variance583.80876
MonotonicityNot monotonic
2023-12-11T08:21:02.378824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 143
11.3%
5 136
 
10.8%
4 126
 
10.0%
2 118
 
9.4%
1 78
 
6.2%
6 76
 
6.0%
10 64
 
5.1%
7 61
 
4.8%
8 56
 
4.4%
9 46
 
3.6%
Other values (74) 357
28.3%
ValueCountFrequency (%)
0 10
 
0.8%
1 78
6.2%
2 118
9.4%
3 143
11.3%
4 126
10.0%
5 136
10.8%
6 76
6.0%
7 61
4.8%
8 56
 
4.4%
9 46
 
3.6%
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

Distinct1004
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2023-12-11T08:21:02.644608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length52
Mean length10.293418
Min length1

Characters and Unicode

Total characters12980
Distinct characters572
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

Unique918 ?
Unique (%)72.8%

Sample

1st rowLED조명기구, 배전반, PCB시장기판 등, LED램프
2nd row육묘상자
3rd row농기계부품
4th row수전금구, 가공타일 등
5th row버섯균사체,공액리놀레산
ValueCountFrequency (%)
등 90
 
3.5%
농기계부품 84
 
3.3%
및 81
 
3.2%
부품 71
 
2.8%
자동차부품 69
 
2.7%
자동차 44
 
1.7%
농기계 25
 
1.0%
중장비 23
 
0.9%
기어 22
 
0.9%
중장비부품 21
 
0.8%
Other values (1451) 2016
79.2%
2023-12-11T08:21:03.026379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1290
 
9.9%
, 779
 
6.0%
기 572
 
4.4%
품 558
 
4.3%
부 495
 
3.8%
자 244
 
1.9%
동 232
 
1.8%
계 226
 
1.7%
차 224
 
1.7%
농 219
 
1.7%
Other values (562) 8141
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10291
79.3%
Space Separator 1290
 
9.9%
Other Punctuation 796
 
6.1%
Uppercase Letter 292
 
2.2%
Open Punctuation 139
 
1.1%
Close Punctuation 137
 
1.1%
Lowercase Letter 20
 
0.2%
Decimal Number 11
 
0.1%
Dash Punctuation 2
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 572
 
5.6%
품 558
 
5.4%
부 495
 
4.8%
자 244
 
2.4%
동 232
 
2.3%
계 226
 
2.2%
차 224
 
2.2%
농 219
 
2.1%
장 209
 
2.0%
제 147
 
1.4%
Other values (513) 7165
69.6%
Uppercase Letter
ValueCountFrequency (%)
C 44
15.1%
T 26
 
8.9%
D 25
 
8.6%
P 24
 
8.2%
E 24
 
8.2%
L 22
 
7.5%
V 21
 
7.2%
S 17
 
5.8%
B 13
 
4.5%
U 11
 
3.8%
Other values (14) 65
22.3%
Lowercase Letter
ValueCountFrequency (%)
l 5
25.0%
a 2
 
10.0%
p 2
 
10.0%
i 2
 
10.0%
e 2
 
10.0%
o 2
 
10.0%
u 1
 
5.0%
d 1
 
5.0%
c 1
 
5.0%
k 1
 
5.0%
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 (%)
, 779
97.9%
. 13
 
1.6%
/ 3
 
0.4%
& 1
 
0.1%
Space Separator
ValueCountFrequency (%)
1290
100.0%
Open Punctuation
ValueCountFrequency (%)
( 139
100.0%
Close Punctuation
ValueCountFrequency (%)
) 137
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10291
79.3%
Common 2377
 
18.3%
Latin 312
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 572
 
5.6%
품 558
 
5.4%
부 495
 
4.8%
자 244
 
2.4%
동 232
 
2.3%
계 226
 
2.2%
차 224
 
2.2%
농 219
 
2.1%
장 209
 
2.0%
제 147
 
1.4%
Other values (513) 7165
69.6%
Latin
ValueCountFrequency (%)
C 44
14.1%
T 26
 
8.3%
D 25
 
8.0%
P 24
 
7.7%
E 24
 
7.7%
L 22
 
7.1%
V 21
 
6.7%
S 17
 
5.4%
B 13
 
4.2%
U 11
 
3.5%
Other values (25) 85
27.2%
Common
ValueCountFrequency (%)
1290
54.3%
, 779
32.8%
( 139
 
5.8%
) 137
 
5.8%
. 13
 
0.5%
3 5
 
0.2%
/ 3
 
0.1%
- 2
 
0.1%
2 2
 
0.1%
6 2
 
0.1%
Other values (4) 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10291
79.3%
ASCII 2689
 
20.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1290
48.0%
, 779
29.0%
( 139
 
5.2%
) 137
 
5.1%
C 44
 
1.6%
T 26
 
1.0%
D 25
 
0.9%
P 24
 
0.9%
E 24
 
0.9%
L 22
 
0.8%
Other values (39) 179
 
6.7%
Hangul
ValueCountFrequency (%)
기 572
 
5.6%
품 558
 
5.4%
부 495
 
4.8%
자 244
 
2.4%
동 232
 
2.3%
계 226
 
2.2%
차 224
 
2.2%
농 219
 
2.1%
장 209
 
2.0%
제 147
 
1.4%
Other values (513) 7165
69.6%

용지면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct944
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2968.0899
Minimum0
Maximum98502
Zeros218
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:03.142722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1230.4
median1151.96
Q33329
95-th percentile10858.1
Maximum98502
Range98502
Interquartile range (IQR)3098.6

Descriptive statistics

Standard deviation6684.5002
Coefficient of variation (CV)2.2521219
Kurtosis99.062835
Mean2968.0899
Median Absolute Deviation (MAD)1151.96
Skewness8.4705743
Sum3742761.3
Variance44682543
MonotonicityNot monotonic
2023-12-11T08:21:03.252731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 218
 
17.3%
1653.0 7
 
0.6%
1655.0 7
 
0.6%
1000.0 5
 
0.4%
330.0 5
 
0.4%
140.89 4
 
0.3%
1100.0 4
 
0.3%
991.7 4
 
0.3%
1642.0 3
 
0.2%
1312.0 3
 
0.2%
Other values (934) 1001
79.4%
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%
49599.1 1
0.1%
41143.0 1
0.1%
33580.3 1
0.1%
31340.0 1
0.1%
27795.2 1
0.1%

제조시설면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1068
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1130.2795
Minimum0
Maximum69206.42
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:03.371818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q1170
median444
Q31051.22
95-th percentile4106
Maximum69206.42
Range69206.42
Interquartile range (IQR)881.22

Descriptive statistics

Standard deviation3020.2903
Coefficient of variation (CV)2.6721623
Kurtosis246.49515
Mean1130.2795
Median Absolute Deviation (MAD)330.4
Skewness13.074653
Sum1425282.5
Variance9122153.3
MonotonicityNot monotonic
2023-12-11T08:21:03.483072image/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%
99.0 7
 
0.6%
165.0 7
 
0.6%
66.0 6
 
0.5%
200.0 6
 
0.5%
300.0 5
 
0.4%
198.0 5
 
0.4%
Other values (1058) 1187
94.1%
ValueCountFrequency (%)
0.0 2
0.2%
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%
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 

Distinct871
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean373.75216
Minimum0
Maximum23951.22
Zeros257
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-11T08:21:03.613608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median95.73
Q3354.24
95-th percentile1381.92
Maximum23951.22
Range23951.22
Interquartile range (IQR)339.24

Descriptive statistics

Standard deviation1068.5764
Coefficient of variation (CV)2.8590509
Kurtosis213.7837
Mean373.75216
Median Absolute Deviation (MAD)95.73
Skewness11.810261
Sum471301.47
Variance1141855.6
MonotonicityNot monotonic
2023-12-11T08:21:03.741692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 257
 
20.4%
33.0 7
 
0.6%
26.4 6
 
0.5%
66.0 6
 
0.5%
40.0 6
 
0.5%
64.24 5
 
0.4%
99.0 5
 
0.4%
50.0 5
 
0.4%
10.0 5
 
0.4%
100.0 5
 
0.4%
Other values (861) 954
75.7%
ValueCountFrequency (%)
0.0 257
20.4%
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%
4.4 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

2023-12-11T08:20:55.296172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:48.936411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.692676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.433727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.149370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.853657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.678306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.447289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.477804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.381854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:20:49.785625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.517685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.221529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.934407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.779926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.762788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.574713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.505386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.158192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.867521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:20:52.014544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.866377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.847129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.674861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.609808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.248711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.953504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.686233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.368551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.115966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.962449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.929319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:20:55.727142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.318690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.025916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.757401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.439901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.214783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.041417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.999375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.853945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.795559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.391749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.094033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.825478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.508564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.307803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.125364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.074155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.940404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.873188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.469729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.167538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.895212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.591004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.404457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.206294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.163786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.024433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.947870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.542538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.263021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.983608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.691921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.496651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.280686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.262575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.107011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:56.024530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:49.619299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:50.349259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.066220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:51.774008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:52.584557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:53.363239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:54.370926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:20:55.212101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:21:03.820142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번단지명남종업원여종업원외국인(남)외국인(여)종업원수용지면적제조시설면적부대시설면적
순번1.0000.2010.0000.0370.0000.0960.0400.1040.0260.066
단지명0.2011.0000.3190.2750.4780.4060.2640.5280.1750.564
남종업원0.0000.3191.0000.6240.5160.2700.9550.7540.7730.761
여종업원0.0370.2750.6241.0000.3880.4860.7130.4750.4670.529
외국인(남)0.0000.4780.5160.3881.0000.5040.6030.2440.4410.523
외국인(여)0.0960.4060.2700.4860.5041.0000.2910.0000.0720.240
종업원수0.0400.2640.9550.7130.6030.2911.0000.7340.7980.788
용지면적0.1040.5280.7540.4750.2440.0000.7341.0000.9120.903
제조시설면적0.0260.1750.7730.4670.4410.0720.7980.9121.0000.934
부대시설면적0.0660.5640.7610.5290.5230.2400.7880.9030.9341.000
2023-12-11T08:21:03.920097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번남종업원여종업원외국인(남)외국인(여)종업원수용지면적제조시설면적부대시설면적단지명
순번1.000-0.184-0.068-0.082-0.049-0.164-0.113-0.093-0.1320.088
남종업원-0.1841.0000.4070.2420.0960.9010.5750.5870.5200.169
여종업원-0.0680.4071.0000.2180.1850.6420.2820.3440.3210.133
외국인(남)-0.0820.2420.2181.0000.5580.4190.3010.4370.2970.164
외국인(여)-0.0490.0960.1850.5581.0000.2100.1470.2350.1890.206
종업원수-0.1640.9010.6420.4190.2101.0000.5930.6310.5590.132
용지면적-0.1130.5750.2820.3010.1470.5931.0000.7940.6960.282
제조시설면적-0.0930.5870.3440.4370.2350.6310.7941.0000.6230.090
부대시설면적-0.1320.5200.3210.2970.1890.5590.6960.6231.0000.331
단지명0.0880.1690.1330.1640.2060.1320.2820.0900.3311.000

Missing values

2023-12-11T08:20:56.143264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:20:56.323637image/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.
2023-12-11T08:20:56.458611image/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
78<NA>(주)경남일보경상남도 진주시 남강로 1065 (상평동, 경남일보)경상남도 진주시 상평동 237-4번지58121,신문 발행업055-751-1005055-757-422248150063일간신문3548.5484.382768.86
89진주상평일반산업단지(주)경남철공경상남도 진주시 공단로 80 (상평동)경상남도 진주시 상평동 222-10번지25112,구조용 금속 판제품 및 공작물 제조업055-752-3205055-758-320571008철구조물1818.2242.022.0
910진주정촌일반산업단지(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
순번단지명회사명공장대표주소(도로명)공장대표주소(지번)업종번호업종명전화번호팩스번호남종업원여종업원외국인(남)외국인(여)종업원수생산품용지면적제조시설면적부대시설면적
12511252<NA>효은철강경상남도 진주시 진성면 동부로 1448 (진성면 동산리 109-3 제2종근린생활시설 ( )경상남도 진주시 진성면 동산리 109-3번지 진성면 동산리 109-3 제2종근린생활시설 (25999, 25995그 외 기타 분류 안된 금속 가공 제품 제조업 외 1 종055-742-7747<NA>5<NA><NA><NA>5농산물운반대, 건조대,파레트랙573.045.4448.72
12521253진주상평일반산업단지효창유리경상남도 진주시 큰들로 95 (상평동) 외 1필지경상남도 진주시 상평동 203-20번지 외 1필지23112, 23119안전유리 제조업 외 1 종055-753-1840055-753-574253<NA><NA>8판유리 및 생활유리2735.21384.672563.316
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