Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells53
Missing cells (%)21.0%
Duplicate rows1
Duplicate rows (%)2.8%
Total size in memory2.2 KiB
Average record size in memory61.7 B

Variable types

Text5
Numeric1
Unsupported1

Dataset

Description경상남도 진주시 소재 행정기관 및 공공기관 정보(읍면동사무소명, 소재지, 건축년도, 규모(연면적(㎡)), 전화번호) 제공
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15013808

Alerts

Dataset has 1 (2.8%) duplicate rowsDuplicates
읍면동명 has 2 (5.6%) missing valuesMissing
사무소명(주민센터)/현장민원실명 has 3 (8.3%) missing valuesMissing
소재지 has 3 (8.3%) missing valuesMissing
건축년도 has 3 (8.3%) missing valuesMissing
규모(전체면적) has 3 (8.3%) missing valuesMissing
전화번호 has 3 (8.3%) missing valuesMissing
비고 has 36 (100.0%) missing valuesMissing
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 23:34:46.003925
Analysis finished2023-12-10 23:34:46.855961
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동명
Text

MISSING 

Distinct32
Distinct (%)94.1%
Missing2
Missing (%)5.6%
Memory size420.0 B
2023-12-11T08:34:47.027996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length3
Mean length4.5588235
Min length3

Characters and Unicode

Total characters155
Distinct characters68
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)88.2%

Sample

1st row진주시
2nd row가호동
3rd row금곡면
4th row금산면
5th row내동면
ValueCountFrequency (%)
천전동 2
 
4.3%
관한 2
 
4.3%
상봉동 2
 
4.3%
『공공기관의 1
 
2.1%
진주시 1
 
2.1%
진성면 1
 
2.1%
지수면 1
 
2.1%
중앙동 1
 
2.1%
정촌면 1
 
2.1%
일반성면 1
 
2.1%
Other values (34) 34
72.3%
2023-12-11T08:34:47.390925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 17
 
11.0%
면 15
 
9.7%
13
 
8.4%
공 5
 
3.2%
대 4
 
2.6%
상 4
 
2.6%
성 4
 
2.6%
천 3
 
1.9%
에 3
 
1.9%
개 3
 
1.9%
Other values (58) 84
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 136
87.7%
Space Separator 13
 
8.4%
Decimal Number 2
 
1.3%
Other Punctuation 2
 
1.3%
Close Punctuation 1
 
0.6%
Open Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
 
12.5%
면 15
 
11.0%
공 5
 
3.7%
대 4
 
2.9%
상 4
 
2.9%
성 4
 
2.9%
천 3
 
2.2%
에 3
 
2.2%
개 3
 
2.2%
평 3
 
2.2%
Other values (51) 75
55.1%
Decimal Number
ValueCountFrequency (%)
9 1
50.0%
6 1
50.0%
Other Punctuation
ValueCountFrequency (%)
※ 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
』 1
100.0%
Open Punctuation
ValueCountFrequency (%)
『 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 136
87.7%
Common 19
 
12.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
 
12.5%
면 15
 
11.0%
공 5
 
3.7%
대 4
 
2.9%
상 4
 
2.9%
성 4
 
2.9%
천 3
 
2.2%
에 3
 
2.2%
개 3
 
2.2%
평 3
 
2.2%
Other values (51) 75
55.1%
Common
ValueCountFrequency (%)
13
68.4%
9 1
 
5.3%
6 1
 
5.3%
』 1
 
5.3%
『 1
 
5.3%
※ 1
 
5.3%
. 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 136
87.7%
ASCII 16
 
10.3%
None 2
 
1.3%
Punctuation 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 17
 
12.5%
면 15
 
11.0%
공 5
 
3.7%
대 4
 
2.9%
상 4
 
2.9%
성 4
 
2.9%
천 3
 
2.2%
에 3
 
2.2%
개 3
 
2.2%
평 3
 
2.2%
Other values (51) 75
55.1%
ASCII
ValueCountFrequency (%)
13
81.2%
9 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
None
ValueCountFrequency (%)
』 1
50.0%
『 1
50.0%
Punctuation
ValueCountFrequency (%)
※ 1
100.0%

사무소명(주민센터)/현장민원실명
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing3
Missing (%)8.3%
Memory size420.0 B
2023-12-11T08:34:47.633177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.3939394
Min length4

Characters and Unicode

Total characters244
Distinct characters58
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

Unique33 ?
Unique (%)100.0%

Sample

1st row진주시청
2nd row가호동행정복지센터
3rd row금곡면사무소
4th row금산면사무소
5th row내동면사무소
ValueCountFrequency (%)
미천면사무소 1
 
2.9%
가호동행정복지센터 1
 
2.9%
하대동행정복지센터 1
 
2.9%
평거동행정복지센터 1
 
2.9%
판문동행정복지센터 1
 
2.9%
충무공동행정복지센터 1
 
2.9%
초장동행정복지센터 1
 
2.9%
칠암현장민원실 1
 
2.9%
천전동행정복지센터 1
 
2.9%
성북동행정복지센터 1
 
2.9%
Other values (24) 24
70.6%
2023-12-11T08:34:47.975164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 17
 
7.0%
무 17
 
7.0%
소 16
 
6.6%
지 15
 
6.1%
면 15
 
6.1%
동 15
 
6.1%
정 15
 
6.1%
센 14
 
5.7%
행 14
 
5.7%
복 14
 
5.7%
Other values (48) 92
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 243
99.6%
Space Separator 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 17
 
7.0%
무 17
 
7.0%
소 16
 
6.6%
지 15
 
6.2%
면 15
 
6.2%
동 15
 
6.2%
정 15
 
6.2%
센 14
 
5.8%
행 14
 
5.8%
복 14
 
5.8%
Other values (47) 91
37.4%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 243
99.6%
Common 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 17
 
7.0%
무 17
 
7.0%
소 16
 
6.6%
지 15
 
6.2%
면 15
 
6.2%
동 15
 
6.2%
정 15
 
6.2%
센 14
 
5.8%
행 14
 
5.8%
복 14
 
5.8%
Other values (47) 91
37.4%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 243
99.6%
ASCII 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 17
 
7.0%
무 17
 
7.0%
소 16
 
6.6%
지 15
 
6.2%
면 15
 
6.2%
동 15
 
6.2%
정 15
 
6.2%
센 14
 
5.8%
행 14
 
5.8%
복 14
 
5.8%
Other values (47) 91
37.4%
ASCII
ValueCountFrequency (%)
1
100.0%

소재지
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing3
Missing (%)8.3%
Memory size420.0 B
2023-12-11T08:34:48.209248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length21.727273
Min length15

Characters and Unicode

Total characters717
Distinct characters89
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row경상남도 진주시 동진로 155(상대동)
2nd row경상남도 진주시 가좌길 74번길 9(가좌동)
3rd row경상남도 진주시 금곡면 월아산로 140
4th row경상남도 진주시 금산면 금산로 107
5th row경남 진주시 내동면 순환로 389
ValueCountFrequency (%)
경상남도 32
21.8%
진주시 32
21.8%
동진로 2
 
1.4%
동부로 2
 
1.4%
평거로 1
 
0.7%
평거로40번길 1
 
0.7%
14(초전동 1
 
0.7%
초장로14번길 1
 
0.7%
145(충무공동 1
 
0.7%
충의로 1
 
0.7%
Other values (73) 73
49.7%
2023-12-11T08:34:48.556288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122
17.0%
진 40
 
5.6%
상 37
 
5.2%
경 35
 
4.9%
남 35
 
4.9%
주 34
 
4.7%
도 32
 
4.5%
시 32
 
4.5%
로 30
 
4.2%
동 24
 
3.3%
Other values (79) 296
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 448
62.5%
Space Separator 122
 
17.0%
Decimal Number 112
 
15.6%
Close Punctuation 17
 
2.4%
Open Punctuation 17
 
2.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
진 40
 
8.9%
상 37
 
8.3%
경 35
 
7.8%
남 35
 
7.8%
주 34
 
7.6%
도 32
 
7.1%
시 32
 
7.1%
로 30
 
6.7%
동 24
 
5.4%
길 14
 
3.1%
Other values (65) 135
30.1%
Decimal Number
ValueCountFrequency (%)
1 22
19.6%
7 13
11.6%
4 12
10.7%
3 12
10.7%
5 12
10.7%
9 11
9.8%
0 11
9.8%
2 8
 
7.1%
8 8
 
7.1%
6 3
 
2.7%
Space Separator
ValueCountFrequency (%)
122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 448
62.5%
Common 269
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
진 40
 
8.9%
상 37
 
8.3%
경 35
 
7.8%
남 35
 
7.8%
주 34
 
7.6%
도 32
 
7.1%
시 32
 
7.1%
로 30
 
6.7%
동 24
 
5.4%
길 14
 
3.1%
Other values (65) 135
30.1%
Common
ValueCountFrequency (%)
122
45.4%
1 22
 
8.2%
) 17
 
6.3%
( 17
 
6.3%
7 13
 
4.8%
4 12
 
4.5%
3 12
 
4.5%
5 12
 
4.5%
9 11
 
4.1%
0 11
 
4.1%
Other values (4) 20
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 448
62.5%
ASCII 269
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122
45.4%
1 22
 
8.2%
) 17
 
6.3%
( 17
 
6.3%
7 13
 
4.8%
4 12
 
4.5%
3 12
 
4.5%
5 12
 
4.5%
9 11
 
4.1%
0 11
 
4.1%
Other values (4) 20
 
7.4%
Hangul
ValueCountFrequency (%)
진 40
 
8.9%
상 37
 
8.3%
경 35
 
7.8%
남 35
 
7.8%
주 34
 
7.6%
도 32
 
7.1%
시 32
 
7.1%
로 30
 
6.7%
동 24
 
5.4%
길 14
 
3.1%
Other values (65) 135
30.1%

건축년도
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)66.7%
Missing3
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean1997.3939
Minimum1972
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:34:48.680793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1972
5-th percentile1979.6
Q11989
median1998
Q32006
95-th percentile2015.4
Maximum2016
Range44
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.426229
Coefficient of variation (CV)0.0057205686
Kurtosis-0.65711494
Mean1997.3939
Median Absolute Deviation (MAD)9
Skewness-0.1635107
Sum65914
Variance130.55871
MonotonicityNot monotonic
2023-12-11T08:34:48.782276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1998 3
 
8.3%
2011 2
 
5.6%
2016 2
 
5.6%
1996 2
 
5.6%
1989 2
 
5.6%
1994 2
 
5.6%
1986 2
 
5.6%
1990 2
 
5.6%
2006 2
 
5.6%
2009 2
 
5.6%
Other values (12) 12
33.3%
(Missing) 3
 
8.3%
ValueCountFrequency (%)
1972 1
2.8%
1979 1
2.8%
1980 1
2.8%
1983 1
2.8%
1986 2
5.6%
1987 1
2.8%
1989 2
5.6%
1990 2
5.6%
1993 1
2.8%
1994 2
5.6%
ValueCountFrequency (%)
2016 2
5.6%
2015 1
2.8%
2011 2
5.6%
2009 2
5.6%
2008 1
2.8%
2006 2
5.6%
2005 1
2.8%
2004 1
2.8%
2001 1
2.8%
1999 1
2.8%

규모(전체면적)
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing3
Missing (%)8.3%
Memory size420.0 B
2023-12-11T08:34:48.981813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.393939
Min length11

Characters and Unicode

Total characters442
Distinct characters20
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

Unique33 ?
Unique (%)100.0%

Sample

1st row[연면적(7,390.8㎡)]
2nd row[연면적(384㎡)]
3rd row[연면적(588.09㎡)]
4th row[연면적(872㎡)]
5th row[연면적(1,103㎡)]
ValueCountFrequency (%)
연면적(367.27㎡ 1
 
3.0%
연면적(861.65㎡ 1
 
3.0%
연면적(841.79㎡ 1
 
3.0%
연면적(1,495.89㎡ 1
 
3.0%
연면적(464㎡ 1
 
3.0%
연면적(722.88㎡ 1
 
3.0%
연면적(715.78㎡ 1
 
3.0%
연면적(1498.62㎡ 1
 
3.0%
연면적(838㎡ 1
 
3.0%
연면적(384㎡ 1
 
3.0%
Other values (23) 23
69.7%
2023-12-11T08:34:49.547801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 33
 
7.5%
연 33
 
7.5%
] 33
 
7.5%
) 33
 
7.5%
㎡ 33
 
7.5%
( 33
 
7.5%
적 33
 
7.5%
면 33
 
7.5%
8 25
 
5.7%
. 22
 
5.0%
Other values (10) 131
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149
33.7%
Other Letter 99
22.4%
Open Punctuation 66
14.9%
Close Punctuation 66
14.9%
Other Symbol 33
 
7.5%
Other Punctuation 29
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 25
16.8%
7 18
12.1%
4 17
11.4%
1 17
11.4%
3 16
10.7%
9 15
10.1%
5 14
9.4%
6 11
7.4%
2 9
 
6.0%
0 7
 
4.7%
Other Letter
ValueCountFrequency (%)
연 33
33.3%
적 33
33.3%
면 33
33.3%
Open Punctuation
ValueCountFrequency (%)
[ 33
50.0%
( 33
50.0%
Close Punctuation
ValueCountFrequency (%)
] 33
50.0%
) 33
50.0%
Other Punctuation
ValueCountFrequency (%)
. 22
75.9%
, 7
 
24.1%
Other Symbol
ValueCountFrequency (%)
㎡ 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 343
77.6%
Hangul 99
 
22.4%

Most frequent character per script

Common
ValueCountFrequency (%)
[ 33
9.6%
] 33
9.6%
) 33
9.6%
㎡ 33
9.6%
( 33
9.6%
8 25
 
7.3%
. 22
 
6.4%
7 18
 
5.2%
4 17
 
5.0%
1 17
 
5.0%
Other values (7) 79
23.0%
Hangul
ValueCountFrequency (%)
연 33
33.3%
적 33
33.3%
면 33
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310
70.1%
Hangul 99
 
22.4%
CJK Compat 33
 
7.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[ 33
10.6%
] 33
10.6%
) 33
10.6%
( 33
10.6%
8 25
 
8.1%
. 22
 
7.1%
7 18
 
5.8%
4 17
 
5.5%
1 17
 
5.5%
3 16
 
5.2%
Other values (6) 63
20.3%
Hangul
ValueCountFrequency (%)
연 33
33.3%
적 33
33.3%
면 33
33.3%
CJK Compat
ValueCountFrequency (%)
㎡ 33
100.0%

전화번호
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing3
Missing (%)8.3%
Memory size420.0 B
2023-12-11T08:34:49.753077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters396
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

Unique33 ?
Unique (%)100.0%

Sample

1st row055-749-2114
2nd row055-749-4531
3rd row055-749-3094
4th row055-749-3795
5th row055-749-3031
ValueCountFrequency (%)
055-749-3850 1
 
3.0%
055-749-3180 1
 
3.0%
055-749-4481 1
 
3.0%
055-749-3151 1
 
3.0%
055-749-3061 1
 
3.0%
055-749-4081 1
 
3.0%
055-749-3731 1
 
3.0%
055-749-3121 1
 
3.0%
055-749-4001 1
 
3.0%
055-749-4531 1
 
3.0%
Other values (23) 23
69.7%
2023-12-11T08:34:50.062563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 74
18.7%
- 66
16.7%
4 54
13.6%
0 48
12.1%
7 38
9.6%
9 38
9.6%
1 33
8.3%
3 22
 
5.6%
8 8
 
2.0%
2 8
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
83.3%
Dash Punctuation 66
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 74
22.4%
4 54
16.4%
0 48
14.5%
7 38
11.5%
9 38
11.5%
1 33
10.0%
3 22
 
6.7%
8 8
 
2.4%
2 8
 
2.4%
6 7
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 74
18.7%
- 66
16.7%
4 54
13.6%
0 48
12.1%
7 38
9.6%
9 38
9.6%
1 33
8.3%
3 22
 
5.6%
8 8
 
2.0%
2 8
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 74
18.7%
- 66
16.7%
4 54
13.6%
0 48
12.1%
7 38
9.6%
9 38
9.6%
1 33
8.3%
3 22
 
5.6%
8 8
 
2.0%
2 8
 
2.0%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

Interactions

2023-12-11T08:34:46.325134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:34:50.146332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명사무소명(주민센터)/현장민원실명소재지건축년도규모(전체면적)전화번호
읍면동명1.0001.0001.0000.8471.0001.000
사무소명(주민센터)/현장민원실명1.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.000
건축년도0.8471.0001.0001.0001.0001.000
규모(전체면적)1.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T08:34:46.473173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:34:46.637305image/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.
2023-12-11T08:34:46.777579image/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

읍면동명사무소명(주민센터)/현장민원실명소재지건축년도규모(전체면적)전화번호비고
0진주시진주시청경상남도 진주시 동진로 155(상대동)2001[연면적(7,390.8㎡)]055-749-2114<NA>
1가호동가호동행정복지센터경상남도 진주시 가좌길 74번길 9(가좌동)1986[연면적(384㎡)]055-749-4531<NA>
2금곡면금곡면사무소경상남도 진주시 금곡면 월아산로 1401979[연면적(588.09㎡)]055-749-3094<NA>
3금산면금산면사무소경상남도 진주시 금산면 금산로 1071987[연면적(872㎡)]055-749-3795<NA>
4내동면내동면사무소경남 진주시 내동면 순환로 3892011[연면적(1,103㎡)]055-749-3031<NA>
5대곡면대곡면사무소경상남도 진주시 대곡면 진의로 10942016[연면적(100㎡)]055-749-3760<NA>
6대평면대평면사무소경상남도 진주시 한들길 441998[연면적(555.31㎡)]055-749-3911<NA>
7명석면명석면 사무소경상남도 진주시 광제산로 371983[연면적(548㎡)]055-749-3881<NA>
8문산읍문산읍사무소경상남도 진주시 문산읍 동부로 587번길 121989[연면적(1440.42㎡)]055-749-3001<NA>
9미천면미천면사무소경상남도 진주시 미천면 어옥로 7312016[연면적(367.27㎡)]055-749-3850<NA>
읍면동명사무소명(주민센터)/현장민원실명소재지건축년도규모(전체면적)전화번호비고
26천전동천전동행정복지센터경상남도 진주시 망경남길 30(망경동)1996[연면적(838㎡)]055-749-4001<NA>
27천전동칠암현장민원실경상남도 진주시 강남로177번길 30-7(칠암동)1994[연면적(687㎡)]055-749-2536<NA>
28초장동초장동행정복지센터경상남도 진주시 초장로14번길 14(초전동)2009[연면적(996.3㎡)]055-749-4401<NA>
29충무공동충무공동행정복지센터경상남도 진주시 충의로 145(충무공동)2015[연면적(1,853.88㎡)]055-749-3971<NA>
30판문동판문동행정복지센터경상남도 진주시 새평거로 55(평거동)2011[연면적(1,171㎡)]055-749-4501<NA>
31평거동평거동행정복지센터경상남도 진주시 평거로40번길 5(평거동)2006[연면적(964㎡)]055-749-4431<NA>
32하대동하대동행정복지센터경상남도 진주시 모덕로 305(하대동)1999[연면적(797.39㎡)]055-749-4251<NA>
33<NA><NA><NA><NA><NA><NA><NA>
34<NA><NA><NA><NA><NA><NA><NA>
35※ 『공공기관의 정보공개에 관한 법률』 제9조 6항에 의거 개인에 관한 사항은 정보 공개 제외.<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

읍면동명사무소명(주민센터)/현장민원실명소재지건축년도규모(전체면적)전화번호# duplicates
0<NA><NA><NA><NA><NA><NA>2