Overview

Dataset statistics

Number of variables14
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.9 KiB
Average record size in memory117.7 B

Variable types

Text6
Numeric6
Categorical2

Alerts

319038 is highly overall correlated with 543964 and 2 other fieldsHigh correlation
543964 is highly overall correlated with 319038 and 2 other fieldsHigh correlation
91 is highly overall correlated with 새마을금고High correlation
1168011400006190004 is highly overall correlated with 319038 and 2 other fieldsHigh correlation
1168011400106190004001956 is highly overall correlated with 319038 and 2 other fieldsHigh correlation
새마을금고 is highly overall correlated with 91High correlation
지점 is highly imbalanced (80.5%)Imbalance
A13389 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:15:02.648150
Analysis finished2023-12-10 06:15:22.535891
Duration19.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

A13389
Text

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:15:22.987674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1194
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)100.0%

Sample

1st rowA02721
2nd rowA04153
3rd rowA13490
4th rowA00299
5th rowA09993
ValueCountFrequency (%)
a02721 1
 
0.5%
a04142 1
 
0.5%
a04009 1
 
0.5%
a03046 1
 
0.5%
a00127 1
 
0.5%
a01500 1
 
0.5%
a00180 1
 
0.5%
a00130 1
 
0.5%
a13494 1
 
0.5%
a13324 1
 
0.5%
Other values (189) 189
95.0%
2023-12-10T15:15:23.738813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 277
23.2%
A 199
16.7%
1 132
11.1%
4 101
 
8.5%
2 97
 
8.1%
3 85
 
7.1%
9 68
 
5.7%
6 67
 
5.6%
5 64
 
5.4%
8 57
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 995
83.3%
Uppercase Letter 199
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 277
27.8%
1 132
13.3%
4 101
 
10.2%
2 97
 
9.7%
3 85
 
8.5%
9 68
 
6.8%
6 67
 
6.7%
5 64
 
6.4%
8 57
 
5.7%
7 47
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
A 199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 995
83.3%
Latin 199
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 277
27.8%
1 132
13.3%
4 101
 
10.2%
2 97
 
9.7%
3 85
 
8.5%
9 68
 
6.8%
6 67
 
6.7%
5 64
 
6.4%
8 57
 
5.7%
7 47
 
4.7%
Latin
ValueCountFrequency (%)
A 199
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 277
23.2%
A 199
16.7%
1 132
11.1%
4 101
 
8.5%
2 97
 
8.1%
3 85
 
7.1%
9 68
 
5.7%
6 67
 
5.6%
5 64
 
5.4%
8 57
 
4.8%

서울개인택시조합금고 강남지점
Text

Distinct176
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:15:24.196018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.0904523
Min length2

Characters and Unicode

Total characters1212
Distinct characters158
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

Unique156 ?
Unique (%)78.4%

Sample

1st row삼성로지점
2nd row역삼중앙
3rd row화곡금고 본점
4th row개포남(출)
5th row서울축산농협 염창동지점
ValueCountFrequency (%)
본점 6
 
2.6%
강북금고 5
 
2.2%
역삼동 3
 
1.3%
강서농협 3
 
1.3%
청담역 3
 
1.3%
서울축산농협 3
 
1.3%
대치동 3
 
1.3%
강서지점 3
 
1.3%
영동농협 3
 
1.3%
대치동지점 3
 
1.3%
Other values (174) 196
84.8%
2023-12-10T15:15:24.927378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 74
 
6.1%
역 72
 
5.9%
점 62
 
5.1%
지 56
 
4.6%
금 42
 
3.5%
터 42
 
3.5%
센 42
 
3.5%
강 36
 
3.0%
32
 
2.6%
융 30
 
2.5%
Other values (148) 724
59.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1139
94.0%
Space Separator 32
 
2.6%
Uppercase Letter 16
 
1.3%
Lowercase Letter 10
 
0.8%
Close Punctuation 5
 
0.4%
Open Punctuation 5
 
0.4%
Decimal Number 4
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 74
 
6.5%
역 72
 
6.3%
점 62
 
5.4%
지 56
 
4.9%
금 42
 
3.7%
터 42
 
3.7%
센 42
 
3.7%
강 36
 
3.2%
융 30
 
2.6%
협 28
 
2.5%
Other values (123) 655
57.5%
Lowercase Letter
ValueCountFrequency (%)
i 1
10.0%
h 1
10.0%
o 1
10.0%
w 1
10.0%
a 1
10.0%
r 1
10.0%
s 1
10.0%
l 1
10.0%
u 1
10.0%
b 1
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 5
31.2%
B 4
25.0%
C 2
 
12.5%
T 1
 
6.2%
W 1
 
6.2%
S 1
 
6.2%
G 1
 
6.2%
M 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 1
25.0%
4 1
25.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1139
94.0%
Common 47
 
3.9%
Latin 26
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 74
 
6.5%
역 72
 
6.3%
점 62
 
5.4%
지 56
 
4.9%
금 42
 
3.7%
터 42
 
3.7%
센 42
 
3.7%
강 36
 
3.2%
융 30
 
2.6%
협 28
 
2.5%
Other values (123) 655
57.5%
Latin
ValueCountFrequency (%)
P 5
19.2%
B 4
15.4%
C 2
 
7.7%
i 1
 
3.8%
h 1
 
3.8%
o 1
 
3.8%
w 1
 
3.8%
T 1
 
3.8%
a 1
 
3.8%
r 1
 
3.8%
Other values (8) 8
30.8%
Common
ValueCountFrequency (%)
32
68.1%
) 5
 
10.6%
( 5
 
10.6%
2 2
 
4.3%
1 1
 
2.1%
. 1
 
2.1%
4 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1139
94.0%
ASCII 73
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 74
 
6.5%
역 72
 
6.3%
점 62
 
5.4%
지 56
 
4.9%
금 42
 
3.7%
터 42
 
3.7%
센 42
 
3.7%
강 36
 
3.2%
융 30
 
2.6%
협 28
 
2.5%
Other values (123) 655
57.5%
ASCII
ValueCountFrequency (%)
32
43.8%
) 5
 
6.8%
( 5
 
6.8%
P 5
 
6.8%
B 4
 
5.5%
C 2
 
2.7%
2 2
 
2.7%
i 1
 
1.4%
h 1
 
1.4%
o 1
 
1.4%
Other values (15) 15
20.5%

서울특별시 강남구 개포로 636
Text

Distinct193
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:15:25.519348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length17.638191
Min length1

Characters and Unicode

Total characters3510
Distinct characters83
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)94.0%

Sample

1st row서울특별시 강남구 삼성로 508
2nd row서울특별시 강남구 역삼로 175
3rd row서울특별시 강서구 까치산로4길 3
4th row서울특별시 강남구 개포로 307
5th row서울특별시 강서구 양천로 706
ValueCountFrequency (%)
서울특별시 198
25.0%
강남구 126
 
15.9%
강서구 50
 
6.3%
강북구 22
 
2.8%
테헤란로 21
 
2.6%
강남대로 13
 
1.6%
강서로 11
 
1.4%
논현로 10
 
1.3%
공항대로 9
 
1.1%
도봉로 9
 
1.1%
Other values (219) 324
40.9%
2023-12-10T15:15:26.378592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
594
16.9%
서 261
 
7.4%
강 225
 
6.4%
구 204
 
5.8%
특 198
 
5.6%
별 198
 
5.6%
시 198
 
5.6%
울 198
 
5.6%
로 196
 
5.6%
남 146
 
4.2%
Other values (73) 1092
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2303
65.6%
Decimal Number 611
 
17.4%
Space Separator 594
 
16.9%
Dash Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 261
11.3%
강 225
9.8%
구 204
8.9%
특 198
8.6%
별 198
8.6%
시 198
8.6%
울 198
8.6%
로 196
8.5%
남 146
 
6.3%
대 43
 
1.9%
Other values (60) 436
18.9%
Decimal Number
ValueCountFrequency (%)
2 92
15.1%
1 88
14.4%
3 76
12.4%
0 63
10.3%
5 60
9.8%
4 58
9.5%
6 52
8.5%
8 46
7.5%
7 44
7.2%
9 32
 
5.2%
Space Separator
ValueCountFrequency (%)
594
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2303
65.6%
Common 1206
34.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 261
11.3%
강 225
9.8%
구 204
8.9%
특 198
8.6%
별 198
8.6%
시 198
8.6%
울 198
8.6%
로 196
8.5%
남 146
 
6.3%
대 43
 
1.9%
Other values (60) 436
18.9%
Common
ValueCountFrequency (%)
594
49.3%
2 92
 
7.6%
1 88
 
7.3%
3 76
 
6.3%
0 63
 
5.2%
5 60
 
5.0%
4 58
 
4.8%
6 52
 
4.3%
8 46
 
3.8%
7 44
 
3.6%
Other values (2) 33
 
2.7%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2303
65.6%
ASCII 1207
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
594
49.2%
2 92
 
7.6%
1 88
 
7.3%
3 76
 
6.3%
0 63
 
5.2%
5 60
 
5.0%
4 58
 
4.8%
6 52
 
4.3%
8 46
 
3.8%
7 44
 
3.6%
Other values (3) 34
 
2.8%
Hangul
ValueCountFrequency (%)
서 261
11.3%
강 225
9.8%
구 204
8.9%
특 198
8.6%
별 198
8.6%
시 198
8.6%
울 198
8.6%
로 196
8.5%
남 146
 
6.3%
대 43
 
1.9%
Other values (60) 436
18.9%

319038
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311083.6
Minimum294453
Maximum319575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:15:26.648625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294453
5-th percentile296741.2
Q1307023.5
median314718
Q3316265.5
95-th percentile317365
Maximum319575
Range25122
Interquartile range (IQR)9242

Descriptive statistics

Standard deviation7810.0324
Coefficient of variation (CV)0.025105895
Kurtosis-0.59317746
Mean311083.6
Median Absolute Deviation (MAD)1554
Skewness-1.1176903
Sum61905637
Variance60996606
MonotonicityNot monotonic
2023-12-10T15:15:26.881232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316468 2
 
1.0%
317208 2
 
1.0%
314529 2
 
1.0%
299017 2
 
1.0%
314578 2
 
1.0%
313723 2
 
1.0%
314398 2
 
1.0%
315003 2
 
1.0%
317520 2
 
1.0%
314654 1
 
0.5%
Other values (180) 180
90.5%
ValueCountFrequency (%)
294453 1
0.5%
294920 1
0.5%
295007 1
0.5%
295184 1
0.5%
295296 1
0.5%
295421 1
0.5%
295692 1
0.5%
296484 1
0.5%
296607 1
0.5%
296635 1
0.5%
ValueCountFrequency (%)
319575 1
0.5%
318812 1
0.5%
317915 1
0.5%
317867 1
0.5%
317833 1
0.5%
317797 1
0.5%
317680 1
0.5%
317520 2
1.0%
317464 1
0.5%
317354 1
0.5%

543964
Real number (ℝ)

HIGH CORRELATION 

Distinct192
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548131.9
Minimum541481
Maximum560725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:15:27.121949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum541481
5-th percentile543333.5
Q1544641
median546417
Q3550537
95-th percentile559890
Maximum560725
Range19244
Interquartile range (IQR)5896

Descriptive statistics

Standard deviation4793.2583
Coefficient of variation (CV)0.0087447169
Kurtosis0.82896374
Mean548131.9
Median Absolute Deviation (MAD)2265
Skewness1.277347
Sum1.0907825 × 108
Variance22975325
MonotonicityNot monotonic
2023-12-10T15:15:27.417519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
543680 2
 
1.0%
545828 2
 
1.0%
544641 2
 
1.0%
545494 2
 
1.0%
551307 2
 
1.0%
543910 2
 
1.0%
543375 2
 
1.0%
549551 1
 
0.5%
557428 1
 
0.5%
543335 1
 
0.5%
Other values (182) 182
91.5%
ValueCountFrequency (%)
541481 1
0.5%
542257 1
0.5%
542523 1
0.5%
542745 1
0.5%
542915 1
0.5%
543004 1
0.5%
543013 1
0.5%
543192 1
0.5%
543194 1
0.5%
543320 1
0.5%
ValueCountFrequency (%)
560725 1
0.5%
560531 1
0.5%
560399 1
0.5%
560349 1
0.5%
560249 1
0.5%
560230 1
0.5%
560144 1
0.5%
560141 1
0.5%
560096 1
0.5%
560088 1
0.5%

278293
Real number (ℝ)

Distinct178
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201159.45
Minimum221
Maximum508825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:15:27.661561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum221
5-th percentile14590.6
Q129434
median264945
Q3311723
95-th percentile414556
Maximum508825
Range508604
Interquartile range (IQR)282289

Descriptive statistics

Standard deviation148967.61
Coefficient of variation (CV)0.74054496
Kurtosis-1.3628368
Mean201159.45
Median Absolute Deviation (MAD)95042
Skewness-0.050134954
Sum40030730
Variance2.219135 × 1010
MonotonicityNot monotonic
2023-12-10T15:15:27.914140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
358761 3
 
1.5%
14711 3
 
1.5%
508825 3
 
1.5%
270733 3
 
1.5%
270459 3
 
1.5%
29434 2
 
1.0%
354450 2
 
1.0%
282921 2
 
1.0%
414556 2
 
1.0%
270718 2
 
1.0%
Other values (168) 174
87.4%
ValueCountFrequency (%)
221 1
0.5%
8216 1
0.5%
10019 1
0.5%
11838 1
0.5%
14392 1
0.5%
14432 1
0.5%
14453 1
0.5%
14524 1
0.5%
14537 1
0.5%
14542 1
0.5%
ValueCountFrequency (%)
508825 3
1.5%
502364 1
 
0.5%
419765 1
 
0.5%
415495 1
 
0.5%
414734 1
 
0.5%
414729 1
 
0.5%
414662 1
 
0.5%
414556 2
1.0%
414546 2
1.0%
414237 1
 
0.5%

91
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.778894
Minimum2
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:15:28.124098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q17
median21
Q325
95-th percentile93
Maximum95
Range93
Interquartile range (IQR)18

Descriptive statistics

Standard deviation32.012645
Coefficient of variation (CV)1.0400843
Kurtosis-0.026003814
Mean30.778894
Median Absolute Deviation (MAD)10
Skewness1.2732372
Sum6125
Variance1024.8095
MonotonicityNot monotonic
2023-12-10T15:15:28.286734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20 32
16.1%
21 31
15.6%
4 31
15.6%
25 18
9.0%
11 15
7.5%
93 14
7.0%
91 12
 
6.0%
3 12
 
6.0%
92 9
 
4.5%
7 6
 
3.0%
Other values (7) 19
9.5%
ValueCountFrequency (%)
2 3
 
1.5%
3 12
 
6.0%
4 31
15.6%
7 6
 
3.0%
11 15
7.5%
20 32
16.1%
21 31
15.6%
23 4
 
2.0%
25 18
9.0%
27 2
 
1.0%
ValueCountFrequency (%)
95 5
 
2.5%
93 14
7.0%
92 9
4.5%
91 12
6.0%
35 1
 
0.5%
34 3
 
1.5%
31 1
 
0.5%
27 2
 
1.0%
25 18
9.0%
23 4
 
2.0%

새마을금고
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
우리은행
32 
신한은행
31 
KB국민은행
31 
KEB하나은행
18 
NH농협은행
15 
Other values (12)
72 

Length

Max length7
Median length6
Mean length5.0502513
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row우리은행
2nd rowKB국민은행
3rd row새마을금고
4th row신한은행
5th row지역농협

Common Values

ValueCountFrequency (%)
우리은행 32
16.1%
신한은행 31
15.6%
KB국민은행 31
15.6%
KEB하나은행 18
9.0%
NH농협은행 15
7.5%
지역농협 14
7.0%
새마을금고 12
 
6.0%
IBK기업은행 12
 
6.0%
신협 9
 
4.5%
SH수협은행 6
 
3.0%
Other values (7) 19
9.5%

Length

2023-12-10T15:15:28.841393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
우리은행 32
16.1%
신한은행 31
15.6%
kb국민은행 31
15.6%
keb하나은행 18
9.0%
nh농협은행 15
7.5%
지역농협 14
7.0%
새마을금고 12
 
6.0%
ibk기업은행 12
 
6.0%
신협 9
 
4.5%
sh수협은행 6
 
3.0%
Other values (7) 19
9.5%

서울 강남구 개포로 636
Text

Distinct196
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:15:29.342035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length42
Mean length24.070352
Min length13

Characters and Unicode

Total characters4790
Distinct characters172
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

Unique193 ?
Unique (%)97.0%

Sample

1st row서울특별시 강남구 삼성동 삼성로 508
2nd row서울특별시 강남구 역삼동 역삼로 175
3rd row서울 강서구 까치산로4길 3
4th row서울 강남구 개포1동 개포로 307
5th row서울특별시 강서구 양천로 706 주상복합태진가람아파트 상가 1층 (염창동)
ValueCountFrequency (%)
서울특별시 152
 
14.4%
강남구 127
 
12.1%
강서구 50
 
4.7%
서울 47
 
4.5%
강북구 22
 
2.1%
역삼동 21
 
2.0%
테헤란로 21
 
2.0%
대치동 17
 
1.6%
논현동 17
 
1.6%
강남대로 13
 
1.2%
Other values (328) 566
53.8%
2023-12-10T15:15:30.048299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
966
20.2%
서 264
 
5.5%
강 230
 
4.8%
동 210
 
4.4%
구 207
 
4.3%
울 199
 
4.2%
로 196
 
4.1%
시 155
 
3.2%
특 152
 
3.2%
별 152
 
3.2%
Other values (162) 2059
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2986
62.3%
Space Separator 966
 
20.2%
Decimal Number 727
 
15.2%
Open Punctuation 33
 
0.7%
Close Punctuation 33
 
0.7%
Uppercase Letter 31
 
0.6%
Dash Punctuation 13
 
0.3%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 264
 
8.8%
강 230
 
7.7%
동 210
 
7.0%
구 207
 
6.9%
울 199
 
6.7%
로 196
 
6.6%
시 155
 
5.2%
특 152
 
5.1%
별 152
 
5.1%
남 148
 
5.0%
Other values (137) 1073
35.9%
Decimal Number
ValueCountFrequency (%)
1 131
18.0%
2 117
16.1%
3 81
11.1%
5 68
9.4%
4 67
9.2%
0 67
9.2%
6 59
8.1%
7 52
 
7.2%
8 50
 
6.9%
9 35
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
F 8
25.8%
P 5
16.1%
I 4
12.9%
A 4
12.9%
T 3
 
9.7%
S 2
 
6.5%
E 2
 
6.5%
V 1
 
3.2%
R 1
 
3.2%
K 1
 
3.2%
Space Separator
ValueCountFrequency (%)
966
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2986
62.3%
Common 1773
37.0%
Latin 31
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 264
 
8.8%
강 230
 
7.7%
동 210
 
7.0%
구 207
 
6.9%
울 199
 
6.7%
로 196
 
6.6%
시 155
 
5.2%
특 152
 
5.1%
별 152
 
5.1%
남 148
 
5.0%
Other values (137) 1073
35.9%
Common
ValueCountFrequency (%)
966
54.5%
1 131
 
7.4%
2 117
 
6.6%
3 81
 
4.6%
5 68
 
3.8%
4 67
 
3.8%
0 67
 
3.8%
6 59
 
3.3%
7 52
 
2.9%
8 50
 
2.8%
Other values (5) 115
 
6.5%
Latin
ValueCountFrequency (%)
F 8
25.8%
P 5
16.1%
I 4
12.9%
A 4
12.9%
T 3
 
9.7%
S 2
 
6.5%
E 2
 
6.5%
V 1
 
3.2%
R 1
 
3.2%
K 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2986
62.3%
ASCII 1804
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
966
53.5%
1 131
 
7.3%
2 117
 
6.5%
3 81
 
4.5%
5 68
 
3.8%
4 67
 
3.7%
0 67
 
3.7%
6 59
 
3.3%
7 52
 
2.9%
8 50
 
2.8%
Other values (15) 146
 
8.1%
Hangul
ValueCountFrequency (%)
서 264
 
8.8%
강 230
 
7.7%
동 210
 
7.0%
구 207
 
6.9%
울 199
 
6.7%
로 196
 
6.6%
시 155
 
5.2%
특 152
 
5.1%
별 152
 
5.1%
남 148
 
5.0%
Other values (137) 1073
35.9%

X
Text

Distinct153
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:15:30.471684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length6.5929648
Min length1

Characters and Unicode

Total characters1312
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique147 ?
Unique (%)73.9%

Sample

1st row20101222
2nd row19920623
3rd rowX
4th row20080526
5th rowX
ValueCountFrequency (%)
x 40
 
20.1%
20170125 4
 
2.0%
19781027 2
 
1.0%
19910718 2
 
1.0%
20080324 2
 
1.0%
20041101 2
 
1.0%
20010622 1
 
0.5%
19830311 1
 
0.5%
19790227 1
 
0.5%
20101222 1
 
0.5%
Other values (143) 143
71.9%
2023-12-10T15:15:31.084064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 300
22.9%
1 266
20.3%
2 209
15.9%
9 171
13.0%
8 80
 
6.1%
7 65
 
5.0%
3 59
 
4.5%
4 48
 
3.7%
X 40
 
3.0%
5 37
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1272
97.0%
Uppercase Letter 40
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 300
23.6%
1 266
20.9%
2 209
16.4%
9 171
13.4%
8 80
 
6.3%
7 65
 
5.1%
3 59
 
4.6%
4 48
 
3.8%
5 37
 
2.9%
6 37
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
X 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1272
97.0%
Latin 40
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 300
23.6%
1 266
20.9%
2 209
16.4%
9 171
13.4%
8 80
 
6.3%
7 65
 
5.1%
3 59
 
4.6%
4 48
 
3.8%
5 37
 
2.9%
6 37
 
2.9%
Latin
ValueCountFrequency (%)
X 40
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 300
22.9%
1 266
20.3%
2 209
15.9%
9 171
13.0%
8 80
 
6.1%
7 65
 
5.0%
3 59
 
4.5%
4 48
 
3.7%
X 40
 
3.0%
5 37
 
2.8%

지점
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
지점
193 
출장소
 
6

Length

Max length3
Median length2
Mean length2.0301508
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지점
2nd row지점
3rd row지점
4th row출장소
5th row지점

Common Values

ValueCountFrequency (%)
지점 193
97.0%
출장소 6
 
3.0%

Length

2023-12-10T15:15:31.266685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:15:31.425935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지점 193
97.0%
출장소 6
 
3.0%

1168011400006190004
Real number (ℝ)

HIGH CORRELATION 

Distinct191
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1593422 × 1018
Minimum1.1305101 × 1018
Maximum1.1680118 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:15:31.604236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1018
5-th percentile1.1305102 × 1018
Q11.1500103 × 1018
median1.1680101 × 1018
Q31.1680106 × 1018
95-th percentile1.1680118 × 1018
Maximum1.1680118 × 1018
Range3.75017 × 1016
Interquartile range (IQR)1.80003 × 1016

Descriptive statistics

Standard deviation1.2749598 × 1016
Coefficient of variation (CV)0.010997269
Kurtosis0.16521098
Mean1.1593422 × 1018
Median Absolute Deviation (MAD)6.9999283 × 1011
Skewness-1.1943929
Sum-9.0985791 × 1018
Variance1.6255224 × 1032
MonotonicityNot monotonic
2023-12-10T15:15:31.809985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1168010500001590000 3
 
1.5%
1168010100007370000 2
 
1.0%
1168010600009450001 2
 
1.0%
1150010200006390011 2
 
1.0%
1168010500001690008 2
 
1.0%
1168010100008370012 2
 
1.0%
1168011800004670014 2
 
1.0%
1168010600010190001 1
 
0.5%
1130510100004500065 1
 
0.5%
1168011800009460016 1
 
0.5%
Other values (181) 181
91.0%
ValueCountFrequency (%)
1130510100001970001 1
0.5%
1130510100002240061 1
0.5%
1130510100003100004 1
0.5%
1130510100003170005 1
0.5%
1130510100004500065 1
0.5%
1130510100004650047 1
0.5%
1130510100007030013 1
0.5%
1130510100008380096 1
0.5%
1130510200004180001 1
0.5%
1130510200004460013 1
0.5%
ValueCountFrequency (%)
1168011800009530001 1
0.5%
1168011800009490003 1
0.5%
1168011800009460016 1
0.5%
1168011800009140002 1
0.5%
1168011800005450014 1
0.5%
1168011800005440003 1
0.5%
1168011800005380001 1
0.5%
1168011800005270000 1
0.5%
1168011800004670018 1
0.5%
1168011800004670017 1
0.5%

1168011400106190004001956
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1593422 × 1024
Minimum1.1305101 × 1024
Maximum1.1680118 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:15:31.998685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1024
5-th percentile1.1305102 × 1024
Q11.1500103 × 1024
median1.1680101 × 1024
Q31.1680106 × 1024
95-th percentile1.1680118 × 1024
Maximum1.1680118 × 1024
Range3.75017 × 1022
Interquartile range (IQR)1.80003 × 1022

Descriptive statistics

Standard deviation1.2749598 × 1022
Coefficient of variation (CV)0.010997269
Kurtosis0.16521048
Mean1.1593422 × 1024
Median Absolute Deviation (MAD)6.9999283 × 1017
Skewness-1.1943927
Sum2.3070909 × 1026
Variance1.6255225 × 1044
MonotonicityNot monotonic
2023-12-10T15:15:32.193451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.16801050010159e+24 6
 
3.0%
1.16801180010467e+24 6
 
3.0%
1.16801010010837e+24 3
 
1.5%
1.16801010010701e+24 2
 
1.0%
1.15001020010639e+24 2
 
1.0%
1.16801050010169e+24 2
 
1.0%
1.16801080010058e+24 2
 
1.0%
1.15001060010719e+24 2
 
1.0%
1.16801010010825e+24 2
 
1.0%
1.15001050010773e+24 2
 
1.0%
Other values (164) 170
85.4%
ValueCountFrequency (%)
1.13051010010197e+24 1
0.5%
1.13051010010224e+24 1
0.5%
1.1305101001031e+24 1
0.5%
1.13051010010317e+24 1
0.5%
1.1305101001045e+24 1
0.5%
1.13051010010465e+24 1
0.5%
1.13051010010703e+24 1
0.5%
1.13051010010838e+24 1
0.5%
1.13051020010418e+24 1
0.5%
1.13051020010446e+24 1
0.5%
ValueCountFrequency (%)
1.16801180010953e+24 1
 
0.5%
1.16801180010949e+24 1
 
0.5%
1.16801180010946e+24 1
 
0.5%
1.16801180010915e+24 1
 
0.5%
1.16801180010545e+24 1
 
0.5%
1.16801180010544e+24 1
 
0.5%
1.16801180010538e+24 1
 
0.5%
1.16801180010527e+24 1
 
0.5%
1.16801180010467e+24 6
3.0%
1.16801180010146e+24 1
 
0.5%

서울특별시 강남구 일원동 619-4번지
Text

Distinct191
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:15:32.582541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length20.894472
Min length17

Characters and Unicode

Total characters4158
Distinct characters61
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

Unique184 ?
Unique (%)92.5%

Sample

1st row서울특별시 강남구 삼성동 157-3번지
2nd row서울특별시 강남구 역삼동 747-29번지
3rd row서울특별시 강서구 화곡동 98-60번지
4th row서울특별시 강남구 개포동 652번지
5th row서울특별시 강서구 염창동 263-2번지
ValueCountFrequency (%)
서울특별시 199
25.0%
강남구 127
16.0%
강서구 50
 
6.3%
역삼동 30
 
3.8%
강북구 22
 
2.8%
대치동 19
 
2.4%
논현동 18
 
2.3%
화곡동 17
 
2.1%
도곡동 15
 
1.9%
삼성동 14
 
1.8%
Other values (204) 285
35.8%
2023-12-10T15:15:33.200930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
597
 
14.4%
서 249
 
6.0%
번 202
 
4.9%
구 200
 
4.8%
지 199
 
4.8%
동 199
 
4.8%
울 199
 
4.8%
강 199
 
4.8%
시 199
 
4.8%
별 199
 
4.8%
Other values (51) 1716
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2588
62.2%
Decimal Number 813
 
19.6%
Space Separator 597
 
14.4%
Dash Punctuation 160
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 249
9.6%
번 202
 
7.8%
구 200
 
7.7%
지 199
 
7.7%
동 199
 
7.7%
울 199
 
7.7%
강 199
 
7.7%
시 199
 
7.7%
별 199
 
7.7%
특 199
 
7.7%
Other values (39) 544
21.0%
Decimal Number
ValueCountFrequency (%)
1 158
19.4%
7 94
11.6%
6 83
10.2%
5 77
9.5%
9 74
9.1%
2 72
8.9%
4 70
8.6%
3 67
8.2%
8 67
8.2%
0 51
 
6.3%
Space Separator
ValueCountFrequency (%)
597
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2588
62.2%
Common 1570
37.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 249
9.6%
번 202
 
7.8%
구 200
 
7.7%
지 199
 
7.7%
동 199
 
7.7%
울 199
 
7.7%
강 199
 
7.7%
시 199
 
7.7%
별 199
 
7.7%
특 199
 
7.7%
Other values (39) 544
21.0%
Common
ValueCountFrequency (%)
597
38.0%
- 160
 
10.2%
1 158
 
10.1%
7 94
 
6.0%
6 83
 
5.3%
5 77
 
4.9%
9 74
 
4.7%
2 72
 
4.6%
4 70
 
4.5%
3 67
 
4.3%
Other values (2) 118
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2588
62.2%
ASCII 1570
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
597
38.0%
- 160
 
10.2%
1 158
 
10.1%
7 94
 
6.0%
6 83
 
5.3%
5 77
 
4.9%
9 74
 
4.7%
2 72
 
4.6%
4 70
 
4.5%
3 67
 
4.3%
Other values (2) 118
 
7.5%
Hangul
ValueCountFrequency (%)
서 249
9.6%
번 202
 
7.8%
구 200
 
7.7%
지 199
 
7.7%
동 199
 
7.7%
울 199
 
7.7%
강 199
 
7.7%
시 199
 
7.7%
별 199
 
7.7%
특 199
 
7.7%
Other values (39) 544
21.0%

Interactions

2023-12-10T15:15:14.071693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:03.637271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:05.557748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:07.297556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:09.495553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:11.698307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:15.503779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:03.756442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:05.688816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:07.646263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:09.620702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:11.856349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:16.585110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:03.890036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:05.798498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:07.731547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:09.753738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:12.030180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:17.523116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:04.023209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:05.909128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:07.819021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:09.870660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:12.204820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:18.594792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:04.167756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:06.017705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:07.913460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:09.993146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:12.342789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:19.889817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:04.313015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:06.166591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:08.098485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:10.140512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:15:12.495030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:15:33.336157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
31903854396427829391새마을금고지점11680114000061900041168011400106190004001956
3190381.0000.7580.6360.2340.2430.0000.9810.981
5439640.7581.0000.8800.2870.2540.0990.9530.953
2782930.6360.8801.0000.1880.4390.1400.8250.825
910.2340.2870.1881.0001.0000.0000.3040.304
새마을금고0.2430.2540.4391.0001.0000.0000.3520.352
지점0.0000.0990.1400.0000.0001.0000.0000.000
11680114000061900040.9810.9530.8250.3040.3520.0001.0001.000
11680114001061900040019560.9810.9530.8250.3040.3520.0001.0001.000
2023-12-10T15:15:33.501748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점새마을금고
지점1.0000.000
새마을금고0.0001.000
2023-12-10T15:15:33.631215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
3190385439642782939111680114000061900041168011400106190004001956새마을금고지점
3190381.000-0.7080.031-0.0760.5910.5910.1070.000
543964-0.7081.000-0.1510.142-0.695-0.6950.0990.068
2782930.031-0.1511.000-0.052-0.092-0.0930.1930.103
91-0.0760.142-0.0521.000-0.258-0.2590.9690.000
11680114000061900040.591-0.695-0.092-0.2581.0001.0000.1910.000
11680114001061900040019560.591-0.695-0.093-0.2591.0001.0000.2250.175
새마을금고0.1070.0990.1930.9690.1910.2251.0000.000
지점0.0000.0680.1030.0000.0000.1750.0001.000

Missing values

2023-12-10T15:15:22.127030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:15:22.428572image/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.

Sample

A13389서울개인택시조합금고 강남지점서울특별시 강남구 개포로 63631903854396427829391새마을금고서울 강남구 개포로 636X지점11680114000061900041168011400106190004001956서울특별시 강남구 일원동 619-4번지
0A02721삼성로지점서울특별시 강남구 삼성로 50831679154548327089520우리은행서울특별시 강남구 삼성동 삼성로 50820101222지점11680105000015700031168010500101570003017119서울특별시 강남구 삼성동 157-3번지
1A04153역삼중앙서울특별시 강남구 역삼로 175315153544167331474KB국민은행서울특별시 강남구 역삼동 역삼로 17519920623지점11680101000074700291168010100107470029024938서울특별시 강남구 역삼동 747-29번지
2A13490화곡금고 본점서울특별시 강서구 까치산로4길 32980525495461527591새마을금고서울 강서구 까치산로4길 3X지점11500103000009800601150010300100980060017990서울특별시 강서구 화곡동 98-60번지
3A00299개포남(출)서울특별시 강남구 개포로 3073167185429152309321신한은행서울 강남구 개포1동 개포로 30720080526출장소11680103000065200001168010300106520000019285서울특별시 강남구 개포동 652번지
4A09993서울축산농협 염창동지점서울특별시 강서구 양천로 7063007305503051459693지역농협서울특별시 강서구 양천로 706 주상복합태진가람아파트 상가 1층 (염창동)X지점11500101000026300021150010100102630002027912서울특별시 강서구 염창동 263-2번지
5A00300개포동서울특별시 강남구 삼성로 363176805433203476684KB국민은행서울특별시 강남구 개포동 삼성로 3619840118지점11680103000018600021168010300101860002019918서울특별시 강남구 개포동 186-2번지
6A02252방화동서울특별시 강서구 양천로 66295184552978167534KB국민은행서울특별시 강서구 방화동 양천로 6619811026지점11500109000056700281150010900105670028004716서울특별시 강서구 방화동 567-28번지
7A04148역삼역서울특별시 강남구 논현로 5313148885450572756654KB국민은행서울특별시 강남구 역삼동 논현로 531 윤성빌딩 1층19890421지점11680101000062800131168010100106280013022743서울특별시 강남구 역삼동 628-13번지
8A01230논현역서울특별시 강남구 강남대로 5463137235458573040825KEB하나은행서울특별시 강남구 논현동 강남대로 546 (논현동) 삼양빌딩19920813지점11680108000012000041168010800101200004007888서울특별시 강남구 논현동 120-4번지
9A00248강북구청서울특별시 강북구 도봉로89길 1331418656009622010121신한은행서울 강북구 수유3동 도봉로89길 1320181126지점11305103000019200591130510300101920059006953서울특별시 강북구 수유동 192-59번지
A13389서울개인택시조합금고 강남지점서울특별시 강남구 개포로 63631903854396427829391새마을금고서울 강남구 개포로 636X지점11680114000061900041168011400106190004001956서울특별시 강남구 일원동 619-4번지
189A00189강남대기업금융2센터서울특별시 강남구 테헤란로 5223172085454942454321신한은행서울 강남구 대치2동 테헤란로 52220180102지점11680106000094500011168010600109450001012097서울특별시 강남구 대치동 945-1번지
190A06069학여울역서울특별시 강남구 영동대로 21631791554443227442325KEB하나은행서울특별시 강남구 대치동 영동대로 216 (대치동)19840705지점11680106000005000451168010600100500045014008서울특별시 강남구 대치동 50-45번지
191A04006양재역금융센터서울특별시 강남구 강남대로 24031480354300436550621신한은행서울 강남구 도곡1동 강남대로 24020130428지점11680118000095300011168011800109530001027731서울특별시 강남구 도곡동 953-1번지
192A01221논현동서울특별시 강남구 언주로 6033150935456463462854KB국민은행서울특별시 강남구 논현동 언주로 60319781026지점11680108000023700101168010800102370010008525서울특별시 강남구 논현동 237-10번지
193A08176영동농협 동역삼지점서울특별시 강남구 역삼로 20431530354417336098293지역농협서울특별시 강남구 역삼로 204X지점11680101000076900011168010100107690001024951서울특별시 강남구 역삼동 769-1번지
194A06810강서농협 목동사거리지점서울특별시 강서구 등촌로 412997395485491543793지역농협서울 강서구 등촌로 41 (화곡동 강서농협목동사거리지점)X지점11500103000077500071150010300107750007020759서울특별시 강서구 화곡동 775-7번지
195A06084한국과학기술회관출장소서울특별시 강남구 테헤란로7길 22314494544724245657SH수협은행서울특별시 강남구 역삼동 테헤란로7길 2220170713출장소11680101000063500021168010100106350004023512서울특별시 강남구 역삼동 635-2번지
196A04968일원동지점서울특별시 강남구 양재대로55길 631957554363027870511NH농협은행서울특별시 강남구 일원동 양재대로 55길 619891213지점11680114000071100011168011400107110001002442서울특별시 강남구 일원동 711-1번지
197A00216강남중앙서울특별시 강남구 강남대로 4023142355445113553584KB국민은행서울특별시 강남구 역삼동 강남대로 402 시계탑빌딩 2층19980914지점11680101000082000111168010100108200011023995서울특별시 강남구 역삼동 820-11번지
198A06807강서농협 가양지점서울특별시 강서구 강서로 4892978205524991638293지역농협서울특별시 강서구 강서로 489X지점11500104000015500041150010400101550004010246서울특별시 강서구 가양동 155-4번지