Overview

Dataset statistics

Number of variables7
Number of observations29
Missing cells36
Missing cells (%)17.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory60.6 B

Variable types

Text6
Categorical1

Alerts

전라북도 문화재수리업자 등록현황(2014.05.31) has 21 (72.4%) missing valuesMissing
Unnamed: 1 has 15 (51.7%) missing valuesMissing
Unnamed: 2 has unique valuesUnique
Unnamed: 3 has unique valuesUnique
Unnamed: 4 has unique valuesUnique
Unnamed: 5 has unique valuesUnique

Reproduction

Analysis started2024-03-13 23:46:27.955468
Analysis finished2024-03-13 23:46:28.506882
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전라북도 문화재수리업자 등록현황(2014.05.31)
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing21
Missing (%)72.4%
Memory size364.0 B
2024-03-14T08:46:28.615472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.625
Min length1

Characters and Unicode

Total characters21
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st row시군
2nd row계
3rd row전주시
4th row군산시
5th row익산시
ValueCountFrequency (%)
시군 1
12.5%
계 1
12.5%
전주시 1
12.5%
군산시 1
12.5%
익산시 1
12.5%
정읍시 1
12.5%
남원시 1
12.5%
고창군 1
12.5%
2024-03-14T08:46:28.871279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 6
28.6%
군 3
14.3%
산 2
 
9.5%
계 1
 
4.8%
전 1
 
4.8%
주 1
 
4.8%
익 1
 
4.8%
정 1
 
4.8%
읍 1
 
4.8%
남 1
 
4.8%
Other values (3) 3
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
28.6%
군 3
14.3%
산 2
 
9.5%
계 1
 
4.8%
전 1
 
4.8%
주 1
 
4.8%
익 1
 
4.8%
정 1
 
4.8%
읍 1
 
4.8%
남 1
 
4.8%
Other values (3) 3
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
28.6%
군 3
14.3%
산 2
 
9.5%
계 1
 
4.8%
전 1
 
4.8%
주 1
 
4.8%
익 1
 
4.8%
정 1
 
4.8%
읍 1
 
4.8%
남 1
 
4.8%
Other values (3) 3
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 6
28.6%
군 3
14.3%
산 2
 
9.5%
계 1
 
4.8%
전 1
 
4.8%
주 1
 
4.8%
익 1
 
4.8%
정 1
 
4.8%
읍 1
 
4.8%
남 1
 
4.8%
Other values (3) 3
14.3%

Unnamed: 1
Text

MISSING 

Distinct7
Distinct (%)50.0%
Missing15
Missing (%)51.7%
Memory size364.0 B
2024-03-14T08:46:29.064531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.7857143
Min length2

Characters and Unicode

Total characters67
Distinct characters23
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

Unique5 ?
Unique (%)35.7%

Sample

1st row업종
2nd row6개업종
3rd row보수단청업
4th row실측설계업
5th row보존과학업
ValueCountFrequency (%)
보수단청업 6
42.9%
보존과학업 3
21.4%
업종 1
 
7.1%
6개업종 1
 
7.1%
실측설계업 1
 
7.1%
식물보호업 1
 
7.1%
문화재감리업 1
 
7.1%
2024-03-14T08:46:29.623085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 14
20.9%
보 10
14.9%
단 6
9.0%
청 6
9.0%
수 6
9.0%
존 3
 
4.5%
과 3
 
4.5%
학 3
 
4.5%
종 2
 
3.0%
물 1
 
1.5%
Other values (13) 13
19.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
98.5%
Decimal Number 1
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 14
21.2%
보 10
15.2%
단 6
9.1%
청 6
9.1%
수 6
9.1%
존 3
 
4.5%
과 3
 
4.5%
학 3
 
4.5%
종 2
 
3.0%
물 1
 
1.5%
Other values (12) 12
18.2%
Decimal Number
ValueCountFrequency (%)
6 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
98.5%
Common 1
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 14
21.2%
보 10
15.2%
단 6
9.1%
청 6
9.1%
수 6
9.1%
존 3
 
4.5%
과 3
 
4.5%
학 3
 
4.5%
종 2
 
3.0%
물 1
 
1.5%
Other values (12) 12
18.2%
Common
ValueCountFrequency (%)
6 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
98.5%
ASCII 1
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 14
21.2%
보 10
15.2%
단 6
9.1%
청 6
9.1%
수 6
9.1%
존 3
 
4.5%
과 3
 
4.5%
학 3
 
4.5%
종 2
 
3.0%
물 1
 
1.5%
Other values (12) 12
18.2%
ASCII
ValueCountFrequency (%)
6 1
100.0%

Unnamed: 2
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-03-14T08:46:29.819112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.8275862
Min length2

Characters and Unicode

Total characters169
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row상호
2nd row27개소
3rd row(유)아산종합건설
4th row㈜조양
5th row㈜토울
ValueCountFrequency (%)
상호 1
 
3.4%
㈜장청 1
 
3.4%
유)건웅종합건설 1
 
3.4%
보림문화재 1
 
3.4%
유)하늘채 1
 
3.4%
유)동문 1
 
3.4%
㈜동강종합건설 1
 
3.4%
㈜남전종합건설 1
 
3.4%
㈜예원종합건설 1
 
3.4%
㈜상락문화재수리 1
 
3.4%
Other values (19) 19
65.5%
2024-03-14T08:46:30.150524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 16
 
9.5%
건 13
 
7.7%
설 9
 
5.3%
종 7
 
4.1%
합 7
 
4.1%
( 6
 
3.6%
유 6
 
3.6%
) 6
 
3.6%
문 5
 
3.0%
재 4
 
2.4%
Other values (66) 90
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
82.2%
Other Symbol 16
 
9.5%
Open Punctuation 6
 
3.6%
Close Punctuation 6
 
3.6%
Decimal Number 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
건 13
 
9.4%
설 9
 
6.5%
종 7
 
5.0%
합 7
 
5.0%
유 6
 
4.3%
문 5
 
3.6%
재 4
 
2.9%
화 4
 
2.9%
전 3
 
2.2%
보 3
 
2.2%
Other values (61) 78
56.1%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
7 1
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 155
91.7%
Common 14
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 16
 
10.3%
건 13
 
8.4%
설 9
 
5.8%
종 7
 
4.5%
합 7
 
4.5%
유 6
 
3.9%
문 5
 
3.2%
재 4
 
2.6%
화 4
 
2.6%
전 3
 
1.9%
Other values (62) 81
52.3%
Common
ValueCountFrequency (%)
( 6
42.9%
) 6
42.9%
2 1
 
7.1%
7 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139
82.2%
None 16
 
9.5%
ASCII 14
 
8.3%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 16
100.0%
Hangul
ValueCountFrequency (%)
건 13
 
9.4%
설 9
 
6.5%
종 7
 
5.0%
합 7
 
5.0%
유 6
 
4.3%
문 5
 
3.6%
재 4
 
2.9%
화 4
 
2.9%
전 3
 
2.2%
보 3
 
2.2%
Other values (61) 78
56.1%
ASCII
ValueCountFrequency (%)
( 6
42.9%
) 6
42.9%
2 1
 
7.1%
7 1
 
7.1%

Unnamed: 3
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-03-14T08:46:30.337625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9310345
Min length1

Characters and Unicode

Total characters85
Distinct characters57
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

Unique29 ?
Unique (%)100.0%

Sample

1st row대표자
2nd row-
3rd row한기수
4th row이호석
5th row김재문
ValueCountFrequency (%)
대표자 1
 
3.4%
전경북 1
 
3.4%
정경호 1
 
3.4%
임선기 1
 
3.4%
박은주 1
 
3.4%
서보근 1
 
3.4%
함병진 1
 
3.4%
유도현 1
 
3.4%
송선희 1
 
3.4%
김두용 1
 
3.4%
Other values (19) 19
65.5%
2024-03-14T08:46:30.615176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 6
 
7.1%
희 4
 
4.7%
용 3
 
3.5%
기 3
 
3.5%
호 3
 
3.5%
선 3
 
3.5%
경 3
 
3.5%
박 3
 
3.5%
유 2
 
2.4%
상 2
 
2.4%
Other values (47) 53
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
98.8%
Dash Punctuation 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 6
 
7.1%
희 4
 
4.8%
용 3
 
3.6%
기 3
 
3.6%
호 3
 
3.6%
선 3
 
3.6%
경 3
 
3.6%
박 3
 
3.6%
유 2
 
2.4%
상 2
 
2.4%
Other values (46) 52
61.9%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
98.8%
Common 1
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 6
 
7.1%
희 4
 
4.8%
용 3
 
3.6%
기 3
 
3.6%
호 3
 
3.6%
선 3
 
3.6%
경 3
 
3.6%
박 3
 
3.6%
유 2
 
2.4%
상 2
 
2.4%
Other values (46) 52
61.9%
Common
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
98.8%
ASCII 1
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 6
 
7.1%
희 4
 
4.8%
용 3
 
3.6%
기 3
 
3.6%
호 3
 
3.6%
선 3
 
3.6%
경 3
 
3.6%
박 3
 
3.6%
유 2
 
2.4%
상 2
 
2.4%
Other values (46) 52
61.9%
ASCII
ValueCountFrequency (%)
- 1
100.0%

Unnamed: 4
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-03-14T08:46:30.811064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length24
Mean length17.586207
Min length1

Characters and Unicode

Total characters510
Distinct characters102
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

Unique29 ?
Unique (%)100.0%

Sample

1st row소재지
2nd row-
3rd row전주시 완산구 태평2길 22 2층 202호
4th row전주시 덕진구 백제대로 670(금암동)
5th row전주시 완산구 인정3길 1 .2층
ValueCountFrequency (%)
전주시 12
 
10.5%
완산구 8
 
7.0%
남원시 5
 
4.4%
덕진구 4
 
3.5%
익산시 3
 
2.6%
군산시 3
 
2.6%
2층 3
 
2.6%
36 3
 
2.6%
고창읍 2
 
1.8%
정읍시 2
 
1.8%
Other values (64) 69
60.5%
2024-03-14T08:46:31.132297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
16.7%
시 26
 
5.1%
2 25
 
4.9%
1 20
 
3.9%
로 19
 
3.7%
산 19
 
3.7%
3 17
 
3.3%
( 13
 
2.5%
동 13
 
2.5%
) 13
 
2.5%
Other values (92) 260
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 289
56.7%
Decimal Number 103
 
20.2%
Space Separator 85
 
16.7%
Open Punctuation 13
 
2.5%
Close Punctuation 13
 
2.5%
Dash Punctuation 5
 
1.0%
Other Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 26
 
9.0%
로 19
 
6.6%
산 19
 
6.6%
동 13
 
4.5%
전 12
 
4.2%
주 12
 
4.2%
구 12
 
4.2%
길 8
 
2.8%
완 8
 
2.8%
호 7
 
2.4%
Other values (76) 153
52.9%
Decimal Number
ValueCountFrequency (%)
2 25
24.3%
1 20
19.4%
3 17
16.5%
0 9
 
8.7%
6 9
 
8.7%
7 8
 
7.8%
4 6
 
5.8%
8 4
 
3.9%
5 3
 
2.9%
9 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 289
56.7%
Common 221
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 26
 
9.0%
로 19
 
6.6%
산 19
 
6.6%
동 13
 
4.5%
전 12
 
4.2%
주 12
 
4.2%
구 12
 
4.2%
길 8
 
2.8%
완 8
 
2.8%
호 7
 
2.4%
Other values (76) 153
52.9%
Common
ValueCountFrequency (%)
85
38.5%
2 25
 
11.3%
1 20
 
9.0%
3 17
 
7.7%
( 13
 
5.9%
) 13
 
5.9%
0 9
 
4.1%
6 9
 
4.1%
7 8
 
3.6%
4 6
 
2.7%
Other values (6) 16
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 289
56.7%
ASCII 221
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
38.5%
2 25
 
11.3%
1 20
 
9.0%
3 17
 
7.7%
( 13
 
5.9%
) 13
 
5.9%
0 9
 
4.1%
6 9
 
4.1%
7 8
 
3.6%
4 6
 
2.7%
Other values (6) 16
 
7.2%
Hangul
ValueCountFrequency (%)
시 26
 
9.0%
로 19
 
6.6%
산 19
 
6.6%
동 13
 
4.5%
전 12
 
4.2%
주 12
 
4.2%
구 12
 
4.2%
길 8
 
2.8%
완 8
 
2.8%
호 7
 
2.4%
Other values (76) 153
52.9%

Unnamed: 5
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-03-14T08:46:31.298419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.5862069
Min length1

Characters and Unicode

Total characters220
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row연락처
2nd row-
3rd row236-6700
4th row251-3771
5th row228-6730
ValueCountFrequency (%)
연락처 1
 
3.4%
225-7515 1
 
3.4%
562-7797 1
 
3.4%
625-5050 1
 
3.4%
626-4111 1
 
3.4%
245-7304 1
 
3.4%
625-3103 1
 
3.4%
632-7320 1
 
3.4%
571-0403 1
 
3.4%
535-9913 1
 
3.4%
Other values (19) 19
65.5%
2024-03-14T08:46:31.585513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 30
13.6%
- 28
12.7%
0 26
11.8%
5 25
11.4%
3 24
10.9%
7 21
9.5%
1 20
9.1%
6 16
7.3%
4 11
 
5.0%
8 10
 
4.5%
Other values (4) 9
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 189
85.9%
Dash Punctuation 28
 
12.7%
Other Letter 3
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 30
15.9%
0 26
13.8%
5 25
13.2%
3 24
12.7%
7 21
11.1%
1 20
10.6%
6 16
8.5%
4 11
 
5.8%
8 10
 
5.3%
9 6
 
3.2%
Other Letter
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 217
98.6%
Hangul 3
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 30
13.8%
- 28
12.9%
0 26
12.0%
5 25
11.5%
3 24
11.1%
7 21
9.7%
1 20
9.2%
6 16
7.4%
4 11
 
5.1%
8 10
 
4.6%
Hangul
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 217
98.6%
Hangul 3
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 30
13.8%
- 28
12.9%
0 26
12.0%
5 25
11.5%
3 24
11.1%
7 21
9.7%
1 20
9.2%
6 16
7.4%
4 11
 
5.1%
8 10
 
4.6%
Hangul
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%

Unnamed: 6
Categorical

Distinct4
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
종합
18 
전문
9 
비고
 
1
-
 
1

Length

Max length2
Median length2
Mean length1.9655172
Min length1

Unique

Unique2 ?
Unique (%)6.9%

Sample

1st row비고
2nd row-
3rd row종합
4th row종합
5th row종합

Common Values

ValueCountFrequency (%)
종합 18
62.1%
전문 9
31.0%
비고 1
 
3.4%
- 1
 
3.4%

Length

2024-03-14T08:46:31.752716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:46:31.865373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종합 18
62.1%
전문 9
31.0%
비고 1
 
3.4%
1
 
3.4%

Correlations

2024-03-14T08:46:31.937764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전라북도 문화재수리업자 등록현황(2014.05.31)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
전라북도 문화재수리업자 등록현황(2014.05.31)1.0001.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0001.0001.0001.000
Unnamed: 41.0001.0001.0001.0001.0001.0001.000
Unnamed: 51.0001.0001.0001.0001.0001.0001.000
Unnamed: 61.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-14T08:46:28.285992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:46:28.377093image/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.
2024-03-14T08:46:28.454534image/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

전라북도 문화재수리업자 등록현황(2014.05.31)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0시군업종상호대표자소재지연락처비고
1계6개업종27개소----
2전주시보수단청업(유)아산종합건설한기수전주시 완산구 태평2길 22 2층 202호236-6700종합
3<NA><NA>㈜조양이호석전주시 덕진구 백제대로 670(금암동)251-3771종합
4<NA><NA>㈜토울김재문전주시 완산구 인정3길 1 .2층228-6730종합
5<NA><NA>㈜혜전건설오무웅전주시 완산구 홍산1길 12(효자동2가 2층)228-0150종합
6<NA><NA>㈜한백건설유용식전주시 덕진구 백제대로 782(우아동 3가)211-9339종합
7<NA>실측설계업㈜길건축사박태우전주시 완산구 서곡5길 14-3276-7200전문
8<NA><NA>아리건축사심재경전주시 완산구 감나무3길 776-2253-7402전문
9<NA><NA>㈜미추홀건축사무소강선중전주시 완산구 화산쳔변 3길 3-4(중화산동 2가)237-0137전문
전라북도 문화재수리업자 등록현황(2014.05.31)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
19<NA><NA>㈜청광건설이상원익산시 서동로 9(인화동 2가)855-1310종합
20정읍시보수단청업㈜상락문화재수리김두용정읍시 수성로 20535-9913종합
21<NA><NA>㈜예원종합건설송선희정읍시 신태인읍 정신로 1133-1(신흥철물2층)571-0403종합
22남원시보수단청업㈜남전종합건설유도현남원시 남문로 467(죽항동)632-7320종합
23<NA><NA>㈜동강종합건설함병진남원시 용성로 11625-3103종합
24<NA><NA>(유)동문서보근남원시 황죽로 37(고죽동)245-7304종합
25<NA><NA>(유)하늘채박은주남원시 사매면 덕오로 101626-4111종합
26<NA>보존과학업보림문화재임선기남원시 시묘길 122625-5050전문
27고창군보수단청업(유)건웅종합건설정경호고창군 고창읍 중앙로 239562-7797종합
28<NA><NA>(유)성내이용구고창군 고창읍 성산로 36 13호(3층, 케이티고창빌딩)562-7304종합