Overview

Dataset statistics

Number of variables17
Number of observations34
Missing cells101
Missing cells (%)17.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory146.9 B

Variable types

Text5
Numeric5
Categorical7

Alerts

기업명영문() has constant value ""Constant
물산업대분류() has constant value ""Constant
물산업분류() has constant value ""Constant
물산업중분류() is highly overall correlated with 설립일() and 3 other fieldsHigh correlation
기업규모1() is highly overall correlated with 사업자등록번호 텍스트() and 7 other fieldsHigh correlation
표준산업분류명() is highly overall correlated with 설립일() and 3 other fieldsHigh correlation
기업규모2() is highly overall correlated with 대표번호() and 5 other fieldsHigh correlation
사업자등록번호 텍스트() is highly overall correlated with 대표번호() and 1 other fieldsHigh correlation
대표번호() is highly overall correlated with 사업자등록번호 텍스트() and 3 other fieldsHigh correlation
설립일() is highly overall correlated with 기업규모1() and 3 other fieldsHigh correlation
매출액() is highly overall correlated with 대표번호() and 5 other fieldsHigh correlation
종업원수() is highly overall correlated with 매출액() and 3 other fieldsHigh correlation
기업규모1() is highly imbalanced (80.9%)Imbalance
기업규모2() is highly imbalanced (80.9%)Imbalance
자본금() is highly imbalanced (80.9%)Imbalance
물산업중분류() is highly imbalanced (80.9%)Imbalance
기업명영문() has 33 (97.1%) missing valuesMissing
대표번호() has 21 (61.8%) missing valuesMissing
설립일() has 21 (61.8%) missing valuesMissing
매출액() has 25 (73.5%) missing valuesMissing
종업원수() has 1 (2.9%) missing valuesMissing
기업명() has unique valuesUnique
사업자등록번호 텍스트() has unique valuesUnique
대표자명() has unique valuesUnique
주소() has unique valuesUnique
종업원수() has 3 (8.8%) zerosZeros

Reproduction

Analysis started2023-12-10 10:14:40.139113
Analysis finished2023-12-10 10:14:47.125588
Duration6.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기업명()
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-10T19:14:47.331330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.5294118
Min length5

Characters and Unicode

Total characters256
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row주식회사 거산
2nd row(유)건국전력
3rd row(유)광명전기
4th row(유)대산전력
5th row(유)대선이씨에프건설
ValueCountFrequency (%)
주 3
 
7.9%
거산 1
 
2.6%
동국이엔씨 1
 
2.6%
주)경도이앤씨 1
 
2.6%
주)경남신호 1
 
2.6%
주)경기전력 1
 
2.6%
주)경기에너지 1
 
2.6%
주)경기기술공사 1
 
2.6%
주)건우전력 1
 
2.6%
유)건국전력 1
 
2.6%
Other values (26) 26
68.4%
2023-12-10T19:14:47.895722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 33
 
12.9%
) 33
 
12.9%
주 19
 
7.4%
유 12
 
4.7%
전 11
 
4.3%
기 8
 
3.1%
지 6
 
2.3%
경 6
 
2.3%
력 6
 
2.3%
자 6
 
2.3%
Other values (63) 116
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
72.7%
Open Punctuation 33
 
12.9%
Close Punctuation 33
 
12.9%
Space Separator 4
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 19
 
10.2%
유 12
 
6.5%
전 11
 
5.9%
기 8
 
4.3%
지 6
 
3.2%
경 6
 
3.2%
력 6
 
3.2%
자 6
 
3.2%
이 5
 
2.7%
사 5
 
2.7%
Other values (60) 102
54.8%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 186
72.7%
Common 70
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 19
 
10.2%
유 12
 
6.5%
전 11
 
5.9%
기 8
 
4.3%
지 6
 
3.2%
경 6
 
3.2%
력 6
 
3.2%
자 6
 
3.2%
이 5
 
2.7%
사 5
 
2.7%
Other values (60) 102
54.8%
Common
ValueCountFrequency (%)
( 33
47.1%
) 33
47.1%
4
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 186
72.7%
ASCII 70
 
27.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 33
47.1%
) 33
47.1%
4
 
5.7%
Hangul
ValueCountFrequency (%)
주 19
 
10.2%
유 12
 
6.5%
전 11
 
5.9%
기 8
 
4.3%
지 6
 
3.2%
경 6
 
3.2%
력 6
 
3.2%
자 6
 
3.2%
이 5
 
2.7%
사 5
 
2.7%
Other values (60) 102
54.8%

기업명영문()
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing33
Missing (%)97.1%
Memory size404.0 B
2023-12-10T19:14:48.197433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters21
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowDasan Energy Co./ Ltd
ValueCountFrequency (%)
dasan 1
25.0%
energy 1
25.0%
co 1
25.0%
ltd 1
25.0%
2023-12-10T19:14:49.025135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3
14.3%
n 2
 
9.5%
a 2
 
9.5%
D 1
 
4.8%
C 1
 
4.8%
t 1
 
4.8%
L 1
 
4.8%
/ 1
 
4.8%
. 1
 
4.8%
o 1
 
4.8%
Other values (7) 7
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12
57.1%
Uppercase Letter 4
 
19.0%
Space Separator 3
 
14.3%
Other Punctuation 2
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2
16.7%
a 2
16.7%
t 1
8.3%
o 1
8.3%
g 1
8.3%
y 1
8.3%
r 1
8.3%
e 1
8.3%
s 1
8.3%
d 1
8.3%
Uppercase Letter
ValueCountFrequency (%)
D 1
25.0%
C 1
25.0%
L 1
25.0%
E 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16
76.2%
Common 5
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2
12.5%
a 2
12.5%
D 1
 
6.2%
C 1
 
6.2%
t 1
 
6.2%
L 1
 
6.2%
o 1
 
6.2%
g 1
 
6.2%
y 1
 
6.2%
r 1
 
6.2%
Other values (4) 4
25.0%
Common
ValueCountFrequency (%)
3
60.0%
/ 1
 
20.0%
. 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3
14.3%
n 2
 
9.5%
a 2
 
9.5%
D 1
 
4.8%
C 1
 
4.8%
t 1
 
4.8%
L 1
 
4.8%
/ 1
 
4.8%
. 1
 
4.8%
o 1
 
4.8%
Other values (7) 7
33.3%

사업자등록번호 텍스트()
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8225904 × 109
Minimum1.2381695 × 109
Maximum6.1586145 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-10T19:14:49.302118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2381695 × 109
5-th percentile1.3656484 × 109
Q13.048138 × 109
median4.0331489 × 109
Q34.1881317 × 109
95-th percentile6.1121527 × 109
Maximum6.1586145 × 109
Range4.920445 × 109
Interquartile range (IQR)1.1399937 × 109

Descriptive statistics

Standard deviation1.3378364 × 109
Coefficient of variation (CV)0.34998164
Kurtosis-0.28738156
Mean3.8225904 × 109
Median Absolute Deviation (MAD)9.7500183 × 108
Skewness-0.091972841
Sum1.2996807 × 1011
Variance1.7898063 × 1018
MonotonicityNot monotonic
2023-12-10T19:14:49.633996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5058106056 1
 
2.9%
1238169505 1
 
2.9%
2178117660 1
 
2.9%
1418126649 1
 
2.9%
3108122856 1
 
2.9%
2288101284 1
 
2.9%
4168155373 1
 
2.9%
3108119370 1
 
2.9%
3038150581 1
 
2.9%
4118167665 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1238169505 1
2.9%
1268188777 1
2.9%
1418126649 1
2.9%
2158617903 1
2.9%
2178117660 1
2.9%
2288101284 1
2.9%
3018158990 1
2.9%
3018172518 1
2.9%
3038150581 1
2.9%
3078100220 1
2.9%
ValueCountFrequency (%)
6158614464 1
2.9%
6138121789 1
2.9%
6098169329 1
2.9%
6088146677 1
2.9%
5158127077 1
2.9%
5058106056 1
2.9%
5028104419 1
2.9%
5028103889 1
2.9%
4188136824 1
2.9%
4188116487 1
2.9%

대표자명()
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-10T19:14:50.030750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.3823529
Min length3

Characters and Unicode

Total characters115
Distinct characters64
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row권철순
2nd row이재수
3rd row유명희
4th row박명환
5th row양병환
ValueCountFrequency (%)
권철순 1
 
2.9%
이재수 1
 
2.9%
이경수 1
 
2.9%
김재연 1
 
2.9%
남기태 1
 
2.9%
송영란 1
 
2.9%
심민섭 1
 
2.9%
김영선 1
 
2.9%
한만조 1
 
2.9%
김세레나 1
 
2.9%
Other values (24) 24
70.6%
2023-12-10T19:14:50.752589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 13
 
11.3%
영 6
 
5.2%
정 4
 
3.5%
선 4
 
3.5%
이 3
 
2.6%
명 3
 
2.6%
재 3
 
2.6%
희 3
 
2.6%
/ 3
 
2.6%
송 3
 
2.6%
Other values (54) 70
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
97.4%
Other Punctuation 3
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 13
 
11.6%
영 6
 
5.4%
정 4
 
3.6%
선 4
 
3.6%
이 3
 
2.7%
명 3
 
2.7%
재 3
 
2.7%
희 3
 
2.7%
송 3
 
2.7%
철 3
 
2.7%
Other values (53) 67
59.8%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
97.4%
Common 3
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 13
 
11.6%
영 6
 
5.4%
정 4
 
3.6%
선 4
 
3.6%
이 3
 
2.7%
명 3
 
2.7%
재 3
 
2.7%
희 3
 
2.7%
송 3
 
2.7%
철 3
 
2.7%
Other values (53) 67
59.8%
Common
ValueCountFrequency (%)
/ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
97.4%
ASCII 3
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 13
 
11.6%
영 6
 
5.4%
정 4
 
3.6%
선 4
 
3.6%
이 3
 
2.7%
명 3
 
2.7%
재 3
 
2.7%
희 3
 
2.7%
송 3
 
2.7%
철 3
 
2.7%
Other values (53) 67
59.8%
ASCII
ValueCountFrequency (%)
/ 3
100.0%

대표번호()
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing21
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean8.6658562 × 108
Minimum3.1321156 × 108
Maximum5.3521461 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-10T19:14:50.962968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1321156 × 108
5-th percentile3.1587929 × 108
Q14.2537862 × 108
median5.4743465 × 108
Q36.32119 × 108
95-th percentile2.521671 × 109
Maximum5.3521461 × 109
Range5.0389346 × 109
Interquartile range (IQR)2.0674038 × 108

Descriptive statistics

Standard deviation1.3528926 × 109
Coefficient of variation (CV)1.561176
Kurtosis12.750073
Mean8.6658562 × 108
Median Absolute Deviation (MAD)87252931
Skewness3.5568306
Sum1.1265613 × 1010
Variance1.8303185 × 1018
MonotonicityNot monotonic
2023-12-10T19:14:51.157402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
547434646 1
 
2.9%
632252347 1
 
2.9%
632118997 1
 
2.9%
634687577 1
 
2.9%
5352146125 1
 
2.9%
537461338 1
 
2.9%
425378620 1
 
2.9%
552212292 1
 
2.9%
335526166 1
 
2.9%
313211557 1
 
2.9%
Other values (3) 3
 
8.8%
(Missing) 21
61.8%
ValueCountFrequency (%)
313211557 1
2.9%
317657771 1
2.9%
335526166 1
2.9%
425378620 1
2.9%
432864202 1
2.9%
537461338 1
2.9%
547434646 1
2.9%
552212292 1
2.9%
552661467 1
2.9%
632118997 1
2.9%
ValueCountFrequency (%)
5352146125 1
2.9%
634687577 1
2.9%
632252347 1
2.9%
632118997 1
2.9%
552661467 1
2.9%
552212292 1
2.9%
547434646 1
2.9%
537461338 1
2.9%
432864202 1
2.9%
425378620 1
2.9%

설립일()
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing21
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean20003076
Minimum19810705
Maximum20131111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-10T19:14:51.358089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19810705
5-th percentile19870953
Q119990616
median20020320
Q320050516
95-th percentile20082993
Maximum20131111
Range320406
Interquartile range (IQR)59900

Descriptive statistics

Standard deviation79220.864
Coefficient of variation (CV)0.003960434
Kurtosis2.1747707
Mean20003076
Median Absolute Deviation (MAD)30196
Skewness-1.116123
Sum2.6003999 × 108
Variance6.2759452 × 109
MonotonicityNot monotonic
2023-12-10T19:14:51.570218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
19911119 1
 
2.9%
19990616 1
 
2.9%
20000229 1
 
2.9%
20050516 1
 
2.9%
20041103 1
 
2.9%
19810705 1
 
2.9%
20131111 1
 
2.9%
20020320 1
 
2.9%
19940723 1
 
2.9%
20010724 1
 
2.9%
Other values (3) 3
 
8.8%
(Missing) 21
61.8%
ValueCountFrequency (%)
19810705 1
2.9%
19911119 1
2.9%
19940723 1
2.9%
19990616 1
2.9%
20000229 1
2.9%
20010724 1
2.9%
20020320 1
2.9%
20031107 1
2.9%
20041103 1
2.9%
20050516 1
2.9%
ValueCountFrequency (%)
20131111 1
2.9%
20050915 1
2.9%
20050801 1
2.9%
20050516 1
2.9%
20041103 1
2.9%
20031107 1
2.9%
20020320 1
2.9%
20010724 1
2.9%
20000229 1
2.9%
19990616 1
2.9%

주소()
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-10T19:14:52.031463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length24
Mean length19.882353
Min length13

Characters and Unicode

Total characters676
Distinct characters130
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row경북 경주시 유림로13번길 125-17
2nd row전북 전주시 덕진구 여암2길 31-4
3rd row전북 김제시 동서로 204
4th row전북 완주군 용진면 구억명덕로 226-4()
5th row전북 김제시 복죽로 557 ()
ValueCountFrequency (%)
전북 8
 
4.8%
경기 5
 
3.0%
전남 5
 
3.0%
경남 3
 
1.8%
전주시 3
 
1.8%
2층 3
 
1.8%
충북 3
 
1.8%
경북 2
 
1.2%
충남 2
 
1.2%
보령시 2
 
1.2%
Other values (125) 130
78.3%
2023-12-10T19:14:52.834129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
133
 
19.7%
1 29
 
4.3%
시 24
 
3.6%
로 24
 
3.6%
4 17
 
2.5%
전 17
 
2.5%
2 17
 
2.5%
5 16
 
2.4%
구 16
 
2.4%
3 15
 
2.2%
Other values (120) 368
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 383
56.7%
Space Separator 133
 
19.7%
Decimal Number 127
 
18.8%
Dash Punctuation 10
 
1.5%
Open Punctuation 8
 
1.2%
Close Punctuation 8
 
1.2%
Other Punctuation 7
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 24
 
6.3%
로 24
 
6.3%
전 17
 
4.4%
구 16
 
4.2%
북 14
 
3.7%
동 14
 
3.7%
경 12
 
3.1%
남 12
 
3.1%
길 12
 
3.1%
산 10
 
2.6%
Other values (105) 228
59.5%
Decimal Number
ValueCountFrequency (%)
1 29
22.8%
4 17
13.4%
2 17
13.4%
5 16
12.6%
3 15
11.8%
7 10
 
7.9%
0 9
 
7.1%
8 7
 
5.5%
6 5
 
3.9%
9 2
 
1.6%
Space Separator
ValueCountFrequency (%)
133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 383
56.7%
Common 293
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 24
 
6.3%
로 24
 
6.3%
전 17
 
4.4%
구 16
 
4.2%
북 14
 
3.7%
동 14
 
3.7%
경 12
 
3.1%
남 12
 
3.1%
길 12
 
3.1%
산 10
 
2.6%
Other values (105) 228
59.5%
Common
ValueCountFrequency (%)
133
45.4%
1 29
 
9.9%
4 17
 
5.8%
2 17
 
5.8%
5 16
 
5.5%
3 15
 
5.1%
- 10
 
3.4%
7 10
 
3.4%
0 9
 
3.1%
( 8
 
2.7%
Other values (5) 29
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 383
56.7%
ASCII 293
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
45.4%
1 29
 
9.9%
4 17
 
5.8%
2 17
 
5.8%
5 16
 
5.5%
3 15
 
5.1%
- 10
 
3.4%
7 10
 
3.4%
0 9
 
3.1%
( 8
 
2.7%
Other values (5) 29
 
9.9%
Hangul
ValueCountFrequency (%)
시 24
 
6.3%
로 24
 
6.3%
전 17
 
4.4%
구 16
 
4.2%
북 14
 
3.7%
동 14
 
3.7%
경 12
 
3.1%
남 12
 
3.1%
길 12
 
3.1%
산 10
 
2.6%
Other values (105) 228
59.5%

기업규모1()
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
102020
33 
<NA>
 
1

Length

Max length6
Median length6
Mean length5.9411765
Min length4

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row102020
2nd row102020
3rd row102020
4th row102020
5th row102020

Common Values

ValueCountFrequency (%)
102020 33
97.1%
<NA> 1
 
2.9%

Length

2023-12-10T19:14:53.079977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:53.263931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102020 33
97.1%
na 1
 
2.9%

기업규모2()
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
중소기업
33 
 
1

Length

Max length4
Median length4
Mean length3.9117647
Min length1

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
중소기업 33
97.1%
1
 
2.9%

Length

2023-12-10T19:14:53.457446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:53.643731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중소기업 33
100.0%

자본금()
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
<NA>
33 
410000000
 
1

Length

Max length9
Median length4
Mean length4.1470588
Min length4

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 33
97.1%
410000000 1
 
2.9%

Length

2023-12-10T19:14:53.834797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:54.006882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 33
97.1%
410000000 1
 
2.9%

매출액()
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)88.9%
Missing25
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean1.0628504 × 1010
Minimum8 × 108
Maximum6.8315534 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-10T19:14:54.177594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8 × 108
5-th percentile8.6 × 108
Q11.291 × 109
median2 × 109
Q34.1 × 109
95-th percentile4.586932 × 1010
Maximum6.8315534 × 1010
Range6.7515534 × 1010
Interquartile range (IQR)2.809 × 109

Descriptive statistics

Standard deviation2.1917988 × 1010
Coefficient of variation (CV)2.0621894
Kurtosis8.3288906
Mean1.0628504 × 1010
Median Absolute Deviation (MAD)1.2 × 109
Skewness2.8611309
Sum9.5656534 × 1010
Variance4.8039819 × 1020
MonotonicityNot monotonic
2023-12-10T19:14:54.438119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2000000000 2
 
5.9%
12200000000 1
 
2.9%
800000000 1
 
2.9%
1291000000 1
 
2.9%
950000000 1
 
2.9%
68315534000 1
 
2.9%
4000000000 1
 
2.9%
4100000000 1
 
2.9%
(Missing) 25
73.5%
ValueCountFrequency (%)
800000000 1
2.9%
950000000 1
2.9%
1291000000 1
2.9%
2000000000 2
5.9%
4000000000 1
2.9%
4100000000 1
2.9%
12200000000 1
2.9%
68315534000 1
2.9%
ValueCountFrequency (%)
68315534000 1
2.9%
12200000000 1
2.9%
4100000000 1
2.9%
4000000000 1
2.9%
2000000000 2
5.9%
1291000000 1
2.9%
950000000 1
2.9%
800000000 1
2.9%

종업원수()
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)75.8%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean15.181818
Minimum0
Maximum87
Zeros3
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-10T19:14:54.721600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median9
Q319
95-th percentile47.4
Maximum87
Range87
Interquartile range (IQR)14

Descriptive statistics

Standard deviation18.782396
Coefficient of variation (CV)1.2371638
Kurtosis8.0527655
Mean15.181818
Median Absolute Deviation (MAD)6
Skewness2.6951402
Sum501
Variance352.77841
MonotonicityNot monotonic
2023-12-10T19:14:54.918995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 3
 
8.8%
6 3
 
8.8%
0 3
 
8.8%
5 3
 
8.8%
30 1
 
2.9%
72 1
 
2.9%
8 1
 
2.9%
13 1
 
2.9%
11 1
 
2.9%
22 1
 
2.9%
Other values (15) 15
44.1%
ValueCountFrequency (%)
0 3
8.8%
1 1
 
2.9%
3 1
 
2.9%
4 3
8.8%
5 3
8.8%
6 3
8.8%
7 1
 
2.9%
8 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
ValueCountFrequency (%)
87 1
2.9%
72 1
2.9%
31 1
2.9%
30 1
2.9%
26 1
2.9%
22 1
2.9%
21 1
2.9%
20 1
2.9%
19 1
2.9%
18 1
2.9%

표준산업분류명()
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
일반전기 공사업
23 
내부 전기배선 공사업
8 
산업플랜트 건설업
 
1
전기장비 제조업
 
1
<NA>
 
1

Length

Max length11
Median length8
Mean length8.6176471
Min length4

Unique

Unique3 ?
Unique (%)8.8%

Sample

1st row내부 전기배선 공사업
2nd row일반전기 공사업
3rd row일반전기 공사업
4th row내부 전기배선 공사업
5th row내부 전기배선 공사업

Common Values

ValueCountFrequency (%)
일반전기 공사업 23
67.6%
내부 전기배선 공사업 8
 
23.5%
산업플랜트 건설업 1
 
2.9%
전기장비 제조업 1
 
2.9%
<NA> 1
 
2.9%

Length

2023-12-10T19:14:55.134165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:55.346174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사업 31
41.3%
일반전기 23
30.7%
내부 8
 
10.7%
전기배선 8
 
10.7%
산업플랜트 1
 
1.3%
건설업 1
 
1.3%
전기장비 1
 
1.3%
제조업 1
 
1.3%
na 1
 
1.3%

물산업대분류()
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
건설 시공
34 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건설 시공
2nd row건설 시공
3rd row건설 시공
4th row건설 시공
5th row건설 시공

Common Values

ValueCountFrequency (%)
건설 시공 34
100.0%

Length

2023-12-10T19:14:55.585737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:55.748298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설 34
50.0%
시공 34
50.0%

물산업중분류()
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
물에너지 건설시공
33 
물어네지 건설시공
 
1

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row물어네지 건설시공
2nd row물에너지 건설시공
3rd row물에너지 건설시공
4th row물에너지 건설시공
5th row물에너지 건설시공

Common Values

ValueCountFrequency (%)
물에너지 건설시공 33
97.1%
물어네지 건설시공 1
 
2.9%

Length

2023-12-10T19:14:55.964905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:56.187223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설시공 34
50.0%
물에너지 33
48.5%
물어네지 1
 
1.5%

물산업분류()
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
기타 물에너지설비공사
34 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타 물에너지설비공사
2nd row기타 물에너지설비공사
3rd row기타 물에너지설비공사
4th row기타 물에너지설비공사
5th row기타 물에너지설비공사

Common Values

ValueCountFrequency (%)
기타 물에너지설비공사 34
100.0%

Length

2023-12-10T19:14:56.383700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:56.592010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 34
50.0%
물에너지설비공사 34
50.0%

주요생산품()
Text

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-10T19:14:56.972456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length29.5
Mean length19.441176
Min length4

Characters and Unicode

Total characters661
Distinct characters98
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)82.4%

Sample

1st row전기공사/소방시설공사
2nd row전기공사/소방공사/신재생에너지공사(태양에너지)/수배전 계장판넬 제조/전기자재 도매
3rd row전기공사
4th row내부전기배선공사
5th row전기공사/경미한공사
ValueCountFrequency (%)
전기공사 4
 
6.7%
도소매 4
 
6.7%
제조 4
 
6.7%
전기공사/통신공사 2
 
3.3%
개발 2
 
3.3%
전기공사/소방시설공사/정보통신공사/기계설비공사/태양광발전장치류/전동기/발전기 1
 
1.7%
전기공사/토목공사 1
 
1.7%
일반전기 1
 
1.7%
공사 1
 
1.7%
전기공사/통신공사/전기통신기자재 1
 
1.7%
Other values (39) 39
65.0%
2023-12-10T19:14:57.529088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 68
 
10.3%
공 67
 
10.1%
/ 62
 
9.4%
전 55
 
8.3%
기 50
 
7.6%
26
 
3.9%
소 21
 
3.2%
신 17
 
2.6%
설 13
 
2.0%
재 13
 
2.0%
Other values (88) 269
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 567
85.8%
Other Punctuation 62
 
9.4%
Space Separator 26
 
3.9%
Close Punctuation 3
 
0.5%
Open Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 68
 
12.0%
공 67
 
11.8%
전 55
 
9.7%
기 50
 
8.8%
소 21
 
3.7%
신 17
 
3.0%
설 13
 
2.3%
재 13
 
2.3%
방 13
 
2.3%
통 11
 
1.9%
Other values (84) 239
42.2%
Other Punctuation
ValueCountFrequency (%)
/ 62
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 567
85.8%
Common 94
 
14.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 68
 
12.0%
공 67
 
11.8%
전 55
 
9.7%
기 50
 
8.8%
소 21
 
3.7%
신 17
 
3.0%
설 13
 
2.3%
재 13
 
2.3%
방 13
 
2.3%
통 11
 
1.9%
Other values (84) 239
42.2%
Common
ValueCountFrequency (%)
/ 62
66.0%
26
27.7%
) 3
 
3.2%
( 3
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 567
85.8%
ASCII 94
 
14.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 68
 
12.0%
공 67
 
11.8%
전 55
 
9.7%
기 50
 
8.8%
소 21
 
3.7%
신 17
 
3.0%
설 13
 
2.3%
재 13
 
2.3%
방 13
 
2.3%
통 11
 
1.9%
Other values (84) 239
42.2%
ASCII
ValueCountFrequency (%)
/ 62
66.0%
26
27.7%
) 3
 
3.2%
( 3
 
3.2%

Interactions

2023-12-10T19:14:45.391360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:42.023408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:42.851607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:43.616928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:44.550657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:45.546339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:42.223198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:42.995733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:43.817457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:44.721375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:45.687637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:42.370044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:43.131292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:43.994898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:44.895407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:45.852468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:42.532924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:43.305632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:44.174301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:45.053905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:46.020733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:42.697638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:43.472687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:44.358294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:45.220207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:14:57.714741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업명()사업자등록번호 텍스트()대표자명()대표번호()설립일()주소()기업규모2()매출액()종업원수()표준산업분류명()물산업중분류()주요생산품()
기업명()1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사업자등록번호 텍스트()1.0001.0001.0000.0000.8081.0000.0001.0000.2030.1750.2190.949
대표자명()1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대표번호()1.0000.0001.0001.0000.0001.000NaNNaN0.3500.2180.0001.000
설립일()1.0000.8081.0000.0001.0001.000NaN1.0000.7970.9231.0000.927
주소()1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기업규모2()1.0000.0001.000NaNNaN1.0001.000NaNNaNNaN0.0001.000
매출액()1.0001.0001.000NaN1.0001.000NaN1.0001.0000.9161.0001.000
종업원수()1.0000.2031.0000.3500.7971.000NaN1.0001.0000.6640.8221.000
표준산업분류명()1.0000.1751.0000.2180.9231.000NaN0.9160.6641.0000.1300.909
물산업중분류()1.0000.2191.0000.0001.0001.0000.0001.0000.8220.1301.0001.000
주요생산품()1.0000.9491.0001.0000.9271.0001.0001.0001.0000.9091.0001.000
2023-12-10T19:14:57.945953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물산업중분류()기업규모1()자본금()표준산업분류명()기업규모2()
물산업중분류()1.0001.000NaN0.0640.000
기업규모1()1.0001.000NaN1.0001.000
자본금()NaNNaN1.000NaNNaN
표준산업분류명()0.0641.000NaN1.0001.000
기업규모2()0.0001.000NaN1.0001.000
2023-12-10T19:14:58.137428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업자등록번호 텍스트()대표번호()설립일()매출액()종업원수()기업규모1()기업규모2()자본금()표준산업분류명()물산업중분류()
사업자등록번호 텍스트()1.0000.692-0.038-0.0750.0631.0000.000NaN0.0810.217
대표번호()0.6921.0000.038-0.812-0.3711.0001.000NaN0.1510.000
설립일()-0.0380.0381.0000.3190.1731.0001.000NaN0.5300.739
매출액()-0.075-0.8120.3191.0000.7561.0001.000NaN0.6250.926
종업원수()0.063-0.3710.1730.7561.0001.0001.000NaN0.4710.582
기업규모1()1.0001.0001.0001.0001.0001.0001.000NaN1.0001.000
기업규모2()0.0001.0001.0001.0001.0001.0001.000NaN1.0000.000
자본금()NaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
표준산업분류명()0.0810.1510.5300.6250.4711.0001.000NaN1.0000.064
물산업중분류()0.2170.0000.7390.9260.5821.0000.000NaN0.0641.000

Missing values

2023-12-10T19:14:46.247729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:14:46.674426image/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.
2023-12-10T19:14:46.960065image/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

기업명()기업명영문()사업자등록번호 텍스트()대표자명()대표번호()설립일()주소()기업규모1()기업규모2()자본금()매출액()종업원수()표준산업분류명()물산업대분류()물산업중분류()물산업분류()주요생산품()
0주식회사 거산<NA>5058106056권철순54743464619911119경북 경주시 유림로13번길 125-17102020중소기업<NA>1220000000030내부 전기배선 공사업건설 시공물어네지 건설시공기타 물에너지설비공사전기공사/소방시설공사
1(유)건국전력<NA>4188116487이재수<NA><NA>전북 전주시 덕진구 여암2길 31-4102020중소기업<NA><NA>19일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/소방공사/신재생에너지공사(태양에너지)/수배전 계장판넬 제조/전기자재 도매
2(유)광명전기<NA>4038130717유명희<NA><NA>전북 김제시 동서로 204102020중소기업<NA><NA>3일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사
3(유)대산전력<NA>4028127543박명환63225234719990616전북 완주군 용진면 구억명덕로 226-4()102020중소기업<NA>80000000010내부 전기배선 공사업건설 시공물에너지 건설시공기타 물에너지설비공사내부전기배선공사
4(유)대선이씨에프건설<NA>4028130922양병환63211899720000229전북 김제시 복죽로 557 ()102020중소기업<NA>20000000004내부 전기배선 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/경미한공사
5(유)부광<NA>4078119596정종균<NA><NA>전남 순천시 서면 청소길 147102020중소기업<NA>12910000006일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/신재생에너지태양광시설공사/전기전자자재 도소매
6(유)비젼테크<NA>4018129403김건중63468757720050516전북 군산시 외항로 11 (산북동)102020중소기업<NA><NA>7내부 전기배선 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사
7(유)영인엔지니어링<NA>4188136824김영철<NA><NA>전북 전주시 덕진구 팔복동2가 33-3 103동 씨호102020중소기업<NA><NA>20일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/배관난방공사/기계설비공사/통신공사
8(유)탑전력공사<NA>4028167133신명철<NA><NA>전북 전주시 완산구 대동로 15102020중소기업<NA>9500000006일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사
9(유)태목<NA>4048129760김세레나<NA><NA>전북 고창군 고창읍 중앙로 215/ 301호102020중소기업<NA><NA>15일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/신재생에너지(태양에너지)공사/알류미늄/플라스틱 도소매
기업명()기업명영문()사업자등록번호 텍스트()대표자명()대표번호()설립일()주소()기업규모1()기업규모2()자본금()매출액()종업원수()표준산업분류명()물산업대분류()물산업중분류()물산업분류()주요생산품()
24(주)강주<NA>4168155373서현정<NA><NA>전남 고흥군 고흥읍 원동남계길 15/ 2층102020중소기업<NA>400000000017일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/전력용기자재 도소매
25(주)거성전력<NA>3108119370이형구<NA><NA>충남 보령시 대천방조제로 68102020중소기업<NA><NA>0일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/전기소방자재 도소매/위생관리용역
26(주)거창<NA>1238169505한만조31321155720010724경기 용인시 처인구 남사면 처인성로 1070102020중소기업<NA><NA>26일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/소방시설공사/정보통신공사/기계설비공사/태양광발전장치류/전동기/발전기 제조
27(주)건우전력<NA>3038150581김영선<NA><NA>충북 충주시 목행산단3로 54102020중소기업<NA><NA>18일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/통신공사/소방공사/신재생에너지전문기업건설/배전반/자동제어 제조/소프트웨어 개발
28(주)경기기술공사<NA>1268188777심민섭31765777120050801경기 광주시 오포읍 세피내길 54-6()102020중소기업<NA>410000000022내부 전기배선 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/전기안전관리 대행
29(주)경기에너지<NA>3018172518송영란43286420220031107충북 보은군 보은읍 둔덕수정로 345()102020중소기업<NA><NA>4일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사
30(주)경기전력<NA>3018158990남기태<NA><NA>충북 청주시 서원구 가장로355번길 132102020중소기업<NA><NA>11일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/소방공사/신재생에너지공사/통신공사/전기자재/소방자재 도소매/비주거용건물 임대/인력파견
31(주)경남신호<NA>6138121789김재연<NA><NA>경남 통영시 동충1길 20102020중소기업<NA><NA>5일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/통신장비 도소매
32(주)경도이앤씨<NA>2158617903이경수<NA><NA>경기 평택시 송탄로 335102020중소기업<NA><NA>13일반전기 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/시설물유지관리공사/소방공사
33(주)경도전기<NA>6098169329김정숙55266146720050915경남 창원시 의창구 사림로158번길 26-28/ 4층(사림동)102020중소기업<NA>20000000008내부 전기배선 공사업건설 시공물에너지 건설시공기타 물에너지설비공사전기공사/통신공사