Overview

Dataset statistics

Number of variables8
Number of observations163
Missing cells5
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory67.8 B

Variable types

Numeric3
Categorical2
Text3

Dataset

Description광주광역시 광산구에 위치한 금융기관 현황 (금융기관, 점포명, 주소, 전화번호, 위도, 경도)에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3081385/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 금융기관High correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
금융기관 is highly overall correlated with 연번High correlation
전화번호 has 2 (1.2%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:07:24.877496
Analysis finished2023-12-12 13:07:26.573449
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct163
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T22:07:26.675943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.1
Q141.5
median82
Q3122.5
95-th percentile154.9
Maximum163
Range162
Interquartile range (IQR)81

Descriptive statistics

Standard deviation47.198164
Coefficient of variation (CV)0.57558736
Kurtosis-1.2
Mean82
Median Absolute Deviation (MAD)41
Skewness0
Sum13366
Variance2227.6667
MonotonicityStrictly increasing
2023-12-12T22:07:26.843838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
104 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
108 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
Other values (153) 153
93.9%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
163 1
0.6%
162 1
0.6%
161 1
0.6%
160 1
0.6%
159 1
0.6%
158 1
0.6%
157 1
0.6%
156 1
0.6%
155 1
0.6%
154 1
0.6%

금융기관
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
농축협　　　　　　　
51 
신협　　　　　　　　
20 
광주　　　　　　　　
17 
NH농협은행　　　　
15 
우체국　　　　　　　
15 
Other values (16)
45 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique8 ?
Unique (%)4.9%

Sample

1st row산업　　　　　　　　
2nd row기업　　　　　　　　
3rd row기업　　　　　　　　
4th row기업　　　　　　　　
5th row기업　　　　　　　　

Common Values

ValueCountFrequency (%)
농축협　　　　　　　 51
31.3%
신협　　　　　　　　 20
 
12.3%
광주　　　　　　　　 17
 
10.4%
NH농협은행　　　　 15
 
9.2%
우체국　　　　　　　 15
 
9.2%
새마을금고　　　　　 11
 
6.7%
기업　　　　　　　　 9
 
5.5%
하나　　　　　　　　 5
 
3.1%
KB국민　　　　　　 3
 
1.8%
수협은행　　　　　　 3
 
1.8%
Other values (11) 14
 
8.6%

Length

2023-12-12T22:07:26.976524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
농축협 51
31.3%
신협 20
 
12.3%
광주 17
 
10.4%
nh농협은행 15
 
9.2%
우체국 15
 
9.2%
새마을금고 11
 
6.7%
기업 9
 
5.5%
하나 5
 
3.1%
kb국민 3
 
1.8%
수협은행 3
 
1.8%
Other values (11) 14
 
8.6%

점포명
Text

Distinct154
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T22:07:27.251953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.0368098
Min length2

Characters and Unicode

Total characters984
Distinct characters130
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)91.4%

Sample

1st row광주(지)
2nd row광산(지)
3rd row하남공단(지)
4th row평동공단
5th row광주수완
ValueCountFrequency (%)
수완 4
 
2.5%
광산 4
 
2.5%
광산구청(출 2
 
1.2%
첨단 2
 
1.2%
광산(지 2
 
1.2%
한마음 1
 
0.6%
비아신협쌍암지점 1
 
0.6%
비아신협 1
 
0.6%
금호타이어 1
 
0.6%
하남 1
 
0.6%
Other values (144) 144
88.3%
2023-12-12T22:07:27.734578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 74
 
7.5%
주 57
 
5.8%
남 42
 
4.3%
지 42
 
4.3%
산 37
 
3.8%
신 32
 
3.3%
동 30
 
3.0%
점 28
 
2.8%
협 28
 
2.8%
하 25
 
2.5%
Other values (120) 589
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 945
96.0%
Close Punctuation 15
 
1.5%
Open Punctuation 15
 
1.5%
Uppercase Letter 6
 
0.6%
Decimal Number 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 74
 
7.8%
주 57
 
6.0%
남 42
 
4.4%
지 42
 
4.4%
산 37
 
3.9%
신 32
 
3.4%
동 30
 
3.2%
점 28
 
3.0%
협 28
 
3.0%
하 25
 
2.6%
Other values (112) 550
58.2%
Uppercase Letter
ValueCountFrequency (%)
N 2
33.3%
H 2
33.3%
Ｗ 1
16.7%
Ｍ 1
16.7%
Decimal Number
ValueCountFrequency (%)
２ 2
66.7%
１ 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 945
96.0%
Common 33
 
3.4%
Latin 6
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 74
 
7.8%
주 57
 
6.0%
남 42
 
4.4%
지 42
 
4.4%
산 37
 
3.9%
신 32
 
3.4%
동 30
 
3.2%
점 28
 
3.0%
협 28
 
3.0%
하 25
 
2.6%
Other values (112) 550
58.2%
Common
ValueCountFrequency (%)
) 15
45.5%
( 15
45.5%
２ 2
 
6.1%
１ 1
 
3.0%
Latin
ValueCountFrequency (%)
N 2
33.3%
H 2
33.3%
Ｗ 1
16.7%
Ｍ 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 945
96.0%
ASCII 34
 
3.5%
None 5
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 74
 
7.8%
주 57
 
6.0%
남 42
 
4.4%
지 42
 
4.4%
산 37
 
3.9%
신 32
 
3.4%
동 30
 
3.2%
점 28
 
3.0%
협 28
 
3.0%
하 25
 
2.6%
Other values (112) 550
58.2%
ASCII
ValueCountFrequency (%)
) 15
44.1%
( 15
44.1%
N 2
 
5.9%
H 2
 
5.9%
None
ValueCountFrequency (%)
２ 2
40.0%
１ 1
20.0%
Ｗ 1
20.0%
Ｍ 1
20.0%

주소
Text

Distinct142
Distinct (%)87.7%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2023-12-12T22:07:28.085491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length32
Mean length23.969136
Min length16

Characters and Unicode

Total characters3883
Distinct characters119
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

Unique129 ?
Unique (%)79.6%

Sample

1st row광주광역시 광산구 무진대로 261 (우산동)
2nd row광주광역시 광산구 무진대로 261 (우산동)
3rd row광주광역시 광산구 하남산단8번로 169 (도천동)
4th row광주광역시 광산구 평동산단로 209 (옥동)
5th row광주광역시 광산구 장신로 140 (수완동)
ValueCountFrequency (%)
광주광역시 162
23.5%
광산구 162
23.5%
무진대로 18
 
2.6%
우산동 14
 
2.0%
장신로 9
 
1.3%
월계로 7
 
1.0%
임방울대로 7
 
1.0%
285 7
 
1.0%
신창로 6
 
0.9%
사암로 5
 
0.7%
Other values (225) 291
42.3%
2023-12-12T22:07:28.544044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
526
 
13.5%
광 495
 
12.7%
산 228
 
5.9%
주 163
 
4.2%
동 163
 
4.2%
시 162
 
4.2%
역 162
 
4.2%
구 162
 
4.2%
로 157
 
4.0%
) 155
 
4.0%
Other values (109) 1510
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2485
64.0%
Decimal Number 540
 
13.9%
Space Separator 526
 
13.5%
Close Punctuation 155
 
4.0%
Open Punctuation 155
 
4.0%
Dash Punctuation 17
 
0.4%
Other Punctuation 3
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 495
19.9%
산 228
 
9.2%
주 163
 
6.6%
동 163
 
6.6%
시 162
 
6.5%
역 162
 
6.5%
구 162
 
6.5%
로 157
 
6.3%
번 48
 
1.9%
대 42
 
1.7%
Other values (92) 703
28.3%
Decimal Number
ValueCountFrequency (%)
1 100
18.5%
2 74
13.7%
3 67
12.4%
7 53
9.8%
5 47
8.7%
9 43
8.0%
6 41
7.6%
0 41
7.6%
8 40
 
7.4%
4 34
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
H 1
50.0%
Space Separator
ValueCountFrequency (%)
526
100.0%
Close Punctuation
ValueCountFrequency (%)
) 155
100.0%
Open Punctuation
ValueCountFrequency (%)
( 155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2485
64.0%
Common 1396
36.0%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 495
19.9%
산 228
 
9.2%
주 163
 
6.6%
동 163
 
6.6%
시 162
 
6.5%
역 162
 
6.5%
구 162
 
6.5%
로 157
 
6.3%
번 48
 
1.9%
대 42
 
1.7%
Other values (92) 703
28.3%
Common
ValueCountFrequency (%)
526
37.7%
) 155
 
11.1%
( 155
 
11.1%
1 100
 
7.2%
2 74
 
5.3%
3 67
 
4.8%
7 53
 
3.8%
5 47
 
3.4%
9 43
 
3.1%
6 41
 
2.9%
Other values (5) 135
 
9.7%
Latin
ValueCountFrequency (%)
L 1
50.0%
H 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2485
64.0%
ASCII 1398
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
526
37.6%
) 155
 
11.1%
( 155
 
11.1%
1 100
 
7.2%
2 74
 
5.3%
3 67
 
4.8%
7 53
 
3.8%
5 47
 
3.4%
9 43
 
3.1%
6 41
 
2.9%
Other values (7) 137
 
9.8%
Hangul
ValueCountFrequency (%)
광 495
19.9%
산 228
 
9.2%
주 163
 
6.6%
동 163
 
6.6%
시 162
 
6.5%
역 162
 
6.5%
구 162
 
6.5%
로 157
 
6.3%
번 48
 
1.9%
대 42
 
1.7%
Other values (92) 703
28.3%

전화번호
Text

MISSING 

Distinct148
Distinct (%)91.9%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
2023-12-12T22:07:28.769990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.993789
Min length11

Characters and Unicode

Total characters1931
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

Unique139 ?
Unique (%)86.3%

Sample

1st row062-958-1000
2nd row062-942-9761
3rd row062-956-1811
4th row062-946-1644
5th row062-962-6501
ValueCountFrequency (%)
062-955-4800 6
 
3.7%
062-603-6450 2
 
1.2%
062-942-5885 2
 
1.2%
062-603-6500 2
 
1.2%
062-954-2104 2
 
1.2%
062-943-6070 2
 
1.2%
062-956-1811 2
 
1.2%
062-949-5600 2
 
1.2%
062-943-9780 2
 
1.2%
062-944-8852 1
 
0.6%
Other values (138) 138
85.7%
2023-12-12T22:07:29.143654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 340
17.6%
- 321
16.6%
2 254
13.2%
6 238
12.3%
9 189
9.8%
5 152
7.9%
4 117
 
6.1%
1 114
 
5.9%
7 70
 
3.6%
3 69
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1610
83.4%
Dash Punctuation 321
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 340
21.1%
2 254
15.8%
6 238
14.8%
9 189
11.7%
5 152
9.4%
4 117
 
7.3%
1 114
 
7.1%
7 70
 
4.3%
3 69
 
4.3%
8 67
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1931
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 340
17.6%
- 321
16.6%
2 254
13.2%
6 238
12.3%
9 189
9.8%
5 152
7.9%
4 117
 
6.1%
1 114
 
5.9%
7 70
 
3.6%
3 69
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1931
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 340
17.6%
- 321
16.6%
2 254
13.2%
6 238
12.3%
9 189
9.8%
5 152
7.9%
4 117
 
6.1%
1 114
 
5.9%
7 70
 
3.6%
3 69
 
3.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)82.1%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean35.175112
Minimum35.098645
Maximum35.222068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T22:07:29.317896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.098645
5-th percentile35.128883
Q135.160364
median35.177962
Q335.191199
95-th percentile35.21947
Maximum35.222068
Range0.12342262
Interquartile range (IQR)0.030835449

Descriptive statistics

Standard deviation0.028233476
Coefficient of variation (CV)0.0008026549
Kurtosis-0.59016432
Mean35.175112
Median Absolute Deviation (MAD)0.016735042
Skewness-0.2053416
Sum5698.3681
Variance0.00079712915
MonotonicityNot monotonic
2023-12-12T22:07:29.467557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1618590880103 8
 
4.9%
35.1612272111008 5
 
3.1%
35.1438897729699 3
 
1.8%
35.2137104492444 3
 
1.8%
35.2172293722188 3
 
1.8%
35.161999429242 2
 
1.2%
35.2096643249941 2
 
1.2%
35.2204463922129 2
 
1.2%
35.1905540516861 2
 
1.2%
35.1890452076018 2
 
1.2%
Other values (123) 130
79.8%
ValueCountFrequency (%)
35.0986450721735 1
0.6%
35.0991828608965 1
0.6%
35.12271567725 1
0.6%
35.1243067955539 1
0.6%
35.1245714222776 1
0.6%
35.1254956029067 1
0.6%
35.1263961463667 2
1.2%
35.1287665863158 1
0.6%
35.1310960043665 1
0.6%
35.1318368832398 1
0.6%
ValueCountFrequency (%)
35.2220676896465 1
0.6%
35.2213902225815 1
0.6%
35.2209795028137 1
0.6%
35.2204463922129 2
1.2%
35.2202118692136 1
0.6%
35.2197964259932 1
0.6%
35.219759198252 1
0.6%
35.2195336945582 1
0.6%
35.2182673463264 1
0.6%
35.2181745318631 1
0.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)82.1%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean126.8059
Minimum126.68039
Maximum126.85248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T22:07:29.630027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68039
5-th percentile126.74264
Q1126.79968
median126.80917
Q3126.82433
95-th percentile126.84265
Maximum126.85248
Range0.17208873
Interquartile range (IQR)0.024647492

Descriptive statistics

Standard deviation0.031435315
Coefficient of variation (CV)0.00024790105
Kurtosis4.9716004
Mean126.8059
Median Absolute Deviation (MAD)0.013900613
Skewness-1.8850166
Sum20542.556
Variance0.00098817904
MonotonicityNot monotonic
2023-12-12T22:07:29.804467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.81156944201 8
 
4.9%
126.806633951281 5
 
3.1%
126.800093033292 3
 
1.8%
126.84527187922 3
 
1.8%
126.842654334316 3
 
1.8%
126.809208075771 2
 
1.2%
126.815271924885 2
 
1.2%
126.842110246642 2
 
1.2%
126.814867322345 2
 
1.2%
126.823949130026 2
 
1.2%
Other values (123) 130
79.8%
ValueCountFrequency (%)
126.680394133004 2
1.2%
126.680946030935 1
0.6%
126.699812604839 1
0.6%
126.700010708105 1
0.6%
126.722185059707 1
0.6%
126.729284947436 1
0.6%
126.729937746735 1
0.6%
126.742622245577 1
0.6%
126.743052267548 1
0.6%
126.760603482609 1
0.6%
ValueCountFrequency (%)
126.852482862081 1
 
0.6%
126.852467391734 1
 
0.6%
126.849322107296 1
 
0.6%
126.848984716544 1
 
0.6%
126.848307302583 1
 
0.6%
126.84527187922 3
1.8%
126.842654334316 3
1.8%
126.842569295524 1
 
0.6%
126.842525553685 1
 
0.6%
126.842110246642 2
1.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-05-08
163 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-08
2nd row2023-05-08
3rd row2023-05-08
4th row2023-05-08
5th row2023-05-08

Common Values

ValueCountFrequency (%)
2023-05-08 163
100.0%

Length

2023-12-12T22:07:29.926992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:30.026385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-08 163
100.0%

Interactions

2023-12-12T22:07:25.822461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:25.163614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:25.515250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:25.980565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:25.268584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:25.616585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:26.087870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:25.403520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:25.725296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:07:30.082901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번금융기관위도경도
연번1.0000.9230.2230.136
금융기관0.9231.0000.0000.000
위도0.2230.0001.0000.897
경도0.1360.0000.8971.000
2023-12-12T22:07:30.181925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도금융기관
연번1.0000.1330.0910.646
위도0.1331.0000.7310.000
경도0.0910.7311.0000.000
금융기관0.6460.0000.0001.000

Missing values

2023-12-12T22:07:26.244356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:07:26.380193image/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.
2023-12-12T22:07:26.491316image/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

연번금융기관점포명주소전화번호위도경도데이터기준일자
01산업광주(지)광주광역시 광산구 무진대로 261 (우산동)062-958-100035.161999126.8092082023-05-08
12기업광산(지)광주광역시 광산구 무진대로 261 (우산동)062-942-976135.161999126.8092082023-05-08
23기업하남공단(지)광주광역시 광산구 하남산단8번로 169 (도천동)062-956-181135.209141126.8142292023-05-08
34기업평동공단광주광역시 광산구 평동산단로 209 (옥동)062-946-164435.125496126.7676912023-05-08
45기업광주수완광주광역시 광산구 장신로 140 (수완동)062-962-650135.190402126.825322023-05-08
56기업호남지역본부광주광역시 광산구 무진대로 240 (우산동)062-949-560035.161227126.8066342023-05-08
67기업호남업무지원센터광주광역시 광산구 무진대로 240 (우산동)062-949-561635.161227126.8066342023-05-08
78기업충청호남그룹광주광역시 광산구 무진대로 240 (우산동)062-949-570035.161227126.8066342023-05-08
89하나광주삼성전자출장소광주광역시 광산구 하남산단6번로 107 (오선동)062-955-808035.20314126.8085052023-05-08
910KB국민첨단종합금융센터광주광역시 광산구 월계로 175 (월계동)062-971-700235.21371126.8452722023-05-08
연번금융기관점포명주소전화번호위도경도데이터기준일자
153154새마을금고송정월곡지점광주광역시 광산구 월곡산정로 75-1(월곡동)062-951-745035.166168126.8104372023-05-08
154155새마을금고송정첨단지점광주광역시 광산구 월계로 108(월계동)062-971-282835.213464126.8372662023-05-08
155156새마을금고삼성전자금고광주지점광주광역시 광산구 하남산단6번로 107(오선동)062-950-621035.20314126.8085052023-05-08
156157새마을금고한마음금고운남지점광주광역시 광산구 목련로273번안길 23(운남동)062-955-642335.177874126.8235862023-05-08
157158새마을금고한마음새마을금고우산지점광주광역시 광산구 금봉로 106-1(우산동)062-944-211035.151859126.8098812023-05-08
158159KB증권광산광주광역시 광산구 사암로 205(우산동)062-450-030035.162783126.8079532023-05-08
159160KB증권첨단라운지광주광역시 광산구 월계로 175(월계동)062-441-510035.21371126.8452722023-05-08
160161NH투자증권수완ＷＭ센터광주광역시 광산구 장신로50번길 4(장덕동)02-1544-000035.190561126.8154242023-05-08
161162신한투자증권수완광주광역시 광산구 임방울대로 342(수완동)062-956-070735.190384126.8243262023-05-08
162163DB금융투자첨단광주광역시 광산구 첨단중앙로 136(월계동)062-975-100035.217229126.8426542023-05-08