Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells9
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory69.3 B

Variable types

Text2
Categorical3
Numeric3

Alerts

area_nm is highly overall correlated with city_gn_gu_cd and 4 other fieldsHigh correlation
city_do_cd is highly overall correlated with city_gn_gu_cd and 4 other fieldsHigh correlation
base_ymd is highly overall correlated with city_gn_gu_cd and 4 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with ypos_la and 3 other fieldsHigh correlation
xpos_lo is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
ypos_la is highly overall correlated with city_gn_gu_cd and 3 other fieldsHigh correlation
city_do_cd is highly imbalanced (80.6%)Imbalance
area_nm is highly imbalanced (80.6%)Imbalance
base_ymd is highly imbalanced (80.6%)Imbalance
city_gn_gu_cd has 3 (3.0%) missing valuesMissing
xpos_lo has 3 (3.0%) missing valuesMissing
ypos_la has 3 (3.0%) missing valuesMissing
entrp_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:12:45.992426
Analysis finished2023-12-10 10:12:49.541329
Duration3.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:12:49.931054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length5.58
Min length2

Characters and Unicode

Total characters558
Distinct characters207
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row라일라라일라
2nd row천봉나전칠기체험관
3rd row임희숙침선방
4th row백학조선민화연구소
5th row마인드플랫폼
ValueCountFrequency (%)
소공지하쇼핑센터점 2
 
1.8%
라일라라일라 1
 
0.9%
천봉나전칠기체험관 1
 
0.9%
예우리공예관 1
 
0.9%
지원전통공예 1
 
0.9%
복희칠보 1
 
0.9%
한국공예관 1
 
0.9%
중앙공예관 1
 
0.9%
예촌 1
 
0.9%
은소 1
 
0.9%
Other values (103) 103
90.4%
2023-12-10T19:12:50.647139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 23
 
4.1%
예 17
 
3.0%
전 16
 
2.9%
리 14
 
2.5%
방 14
 
2.5%
14
 
2.5%
통 13
 
2.3%
보 10
 
1.8%
지 10
 
1.8%
소 10
 
1.8%
Other values (197) 417
74.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 525
94.1%
Space Separator 14
 
2.5%
Lowercase Letter 13
 
2.3%
Decimal Number 6
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 23
 
4.4%
예 17
 
3.2%
전 16
 
3.0%
리 14
 
2.7%
방 14
 
2.7%
통 13
 
2.5%
보 10
 
1.9%
지 10
 
1.9%
소 10
 
1.9%
점 9
 
1.7%
Other values (182) 389
74.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
30.8%
u 1
 
7.7%
l 1
 
7.7%
o 1
 
7.7%
s 1
 
7.7%
n 1
 
7.7%
b 1
 
7.7%
v 1
 
7.7%
a 1
 
7.7%
h 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
7 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 525
94.1%
Common 20
 
3.6%
Latin 13
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 23
 
4.4%
예 17
 
3.2%
전 16
 
3.0%
리 14
 
2.7%
방 14
 
2.7%
통 13
 
2.5%
보 10
 
1.9%
지 10
 
1.9%
소 10
 
1.9%
점 9
 
1.7%
Other values (182) 389
74.1%
Latin
ValueCountFrequency (%)
e 4
30.8%
u 1
 
7.7%
l 1
 
7.7%
o 1
 
7.7%
s 1
 
7.7%
n 1
 
7.7%
b 1
 
7.7%
v 1
 
7.7%
a 1
 
7.7%
h 1
 
7.7%
Common
ValueCountFrequency (%)
14
70.0%
7 2
 
10.0%
0 2
 
10.0%
1 1
 
5.0%
2 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 525
94.1%
ASCII 33
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 23
 
4.4%
예 17
 
3.2%
전 16
 
3.0%
리 14
 
2.7%
방 14
 
2.7%
통 13
 
2.5%
보 10
 
1.9%
지 10
 
1.9%
소 10
 
1.9%
점 9
 
1.7%
Other values (182) 389
74.1%
ASCII
ValueCountFrequency (%)
14
42.4%
e 4
 
12.1%
7 2
 
6.1%
0 2
 
6.1%
1 1
 
3.0%
2 1
 
3.0%
u 1
 
3.0%
l 1
 
3.0%
o 1
 
3.0%
s 1
 
3.0%
Other values (5) 5
 
15.2%

load_addr
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:12:51.140376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length36
Mean length22.78
Min length16

Characters and Unicode

Total characters2278
Distinct characters185
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

Unique92 ?
Unique (%)92.0%

Sample

1st row서울특별시 강남구 개포로 241 선모빌딩 5층
2nd row경기도 양주시 장흥면 석현리 394
3rd row서울특별시 강남구 개포로22길 19
4th row서울특별시 강남구 도산대로11길 46 1층
5th row서울특별시 강남구 도산대로81길 5 세영빌딩 3층
ValueCountFrequency (%)
서울특별시 97
 
20.0%
종로구 42
 
8.7%
인사동길 14
 
2.9%
강남구 9
 
1.9%
중구 9
 
1.9%
1층 6
 
1.2%
종로 6
 
1.2%
용산구 5
 
1.0%
은평구 5
 
1.0%
창경궁로 4
 
0.8%
Other values (228) 288
59.4%
2023-12-10T19:12:51.826824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
388
 
17.0%
로 118
 
5.2%
서 110
 
4.8%
시 104
 
4.6%
구 100
 
4.4%
특 98
 
4.3%
울 98
 
4.3%
별 97
 
4.3%
1 81
 
3.6%
2 65
 
2.9%
Other values (175) 1019
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1465
64.3%
Space Separator 388
 
17.0%
Decimal Number 385
 
16.9%
Dash Punctuation 27
 
1.2%
Uppercase Letter 8
 
0.4%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 118
 
8.1%
서 110
 
7.5%
시 104
 
7.1%
구 100
 
6.8%
특 98
 
6.7%
울 98
 
6.7%
별 97
 
6.6%
길 56
 
3.8%
종 51
 
3.5%
동 36
 
2.5%
Other values (155) 597
40.8%
Decimal Number
ValueCountFrequency (%)
1 81
21.0%
2 65
16.9%
4 45
11.7%
3 36
9.4%
0 35
9.1%
5 32
 
8.3%
8 27
 
7.0%
7 24
 
6.2%
9 23
 
6.0%
6 17
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
N 2
25.0%
A 2
25.0%
F 2
25.0%
E 1
12.5%
H 1
12.5%
Space Separator
ValueCountFrequency (%)
388
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1465
64.3%
Common 804
35.3%
Latin 9
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 118
 
8.1%
서 110
 
7.5%
시 104
 
7.1%
구 100
 
6.8%
특 98
 
6.7%
울 98
 
6.7%
별 97
 
6.6%
길 56
 
3.8%
종 51
 
3.5%
동 36
 
2.5%
Other values (155) 597
40.8%
Common
ValueCountFrequency (%)
388
48.3%
1 81
 
10.1%
2 65
 
8.1%
4 45
 
5.6%
3 36
 
4.5%
0 35
 
4.4%
5 32
 
4.0%
8 27
 
3.4%
- 27
 
3.4%
7 24
 
3.0%
Other values (4) 44
 
5.5%
Latin
ValueCountFrequency (%)
N 2
22.2%
A 2
22.2%
F 2
22.2%
E 1
11.1%
H 1
11.1%
a 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1465
64.3%
ASCII 813
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
388
47.7%
1 81
 
10.0%
2 65
 
8.0%
4 45
 
5.5%
3 36
 
4.4%
0 35
 
4.3%
5 32
 
3.9%
8 27
 
3.3%
- 27
 
3.3%
7 24
 
3.0%
Other values (10) 53
 
6.5%
Hangul
ValueCountFrequency (%)
로 118
 
8.1%
서 110
 
7.5%
시 104
 
7.1%
구 100
 
6.8%
특 98
 
6.7%
울 98
 
6.7%
별 97
 
6.6%
길 56
 
3.8%
종 51
 
3.5%
동 36
 
2.5%
Other values (155) 597
40.8%

city_do_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
97 
<NA>
 
3

Length

Max length4
Median length2
Mean length2.06
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row<NA>
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T19:12:52.088813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:52.276236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 97
97.0%
na 3
 
3.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)18.6%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean11284.588
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:52.438219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111110
median11140
Q311440
95-th percentile11680
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation224.96026
Coefficient of variation (CV)0.019935178
Kurtosis-0.87463896
Mean11284.588
Median Absolute Deviation (MAD)30
Skewness0.89626803
Sum1094605
Variance50607.12
MonotonicityNot monotonic
2023-12-10T19:12:52.650717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
11110 42
42.0%
11680 9
 
9.0%
11140 9
 
9.0%
11380 5
 
5.0%
11170 5
 
5.0%
11560 4
 
4.0%
11200 3
 
3.0%
11710 3
 
3.0%
11650 3
 
3.0%
11410 3
 
3.0%
Other values (8) 11
 
11.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
11110 42
42.0%
11140 9
 
9.0%
11170 5
 
5.0%
11200 3
 
3.0%
11215 1
 
1.0%
11230 2
 
2.0%
11290 1
 
1.0%
11350 1
 
1.0%
11380 5
 
5.0%
11410 3
 
3.0%
ValueCountFrequency (%)
11740 1
 
1.0%
11710 3
 
3.0%
11680 9
9.0%
11650 3
 
3.0%
11620 2
 
2.0%
11560 4
4.0%
11530 1
 
1.0%
11440 2
 
2.0%
11410 3
 
3.0%
11380 5
5.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct88
Distinct (%)90.7%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean126.99098
Minimum126.884
Maximum127.13128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:52.885131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.884
5-th percentile126.91773
Q1126.97762
median126.98601
Q3127.00278
95-th percentile127.0663
Maximum127.13128
Range0.247279
Interquartile range (IQR)0.025164

Descriptive statistics

Standard deviation0.047630351
Coefficient of variation (CV)0.00037506879
Kurtosis1.0492041
Mean126.99098
Median Absolute Deviation (MAD)0.014209
Skewness0.36506248
Sum12318.125
Variance0.0022686504
MonotonicityNot monotonic
2023-12-10T19:12:53.115517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.979665 2
 
2.0%
127.002289 2
 
2.0%
126.977724 2
 
2.0%
126.977215 2
 
2.0%
127.000043 2
 
2.0%
126.979231 2
 
2.0%
126.984455 2
 
2.0%
126.99989 2
 
2.0%
126.986768 2
 
2.0%
126.985626 1
 
1.0%
Other values (78) 78
78.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
126.883999 1
1.0%
126.892358 1
1.0%
126.897577 1
1.0%
126.897822 1
1.0%
126.907435 1
1.0%
126.920302 1
1.0%
126.920701 1
1.0%
126.920874 1
1.0%
126.921759 1
1.0%
126.922934 1
1.0%
ValueCountFrequency (%)
127.131278 1
1.0%
127.121709 1
1.0%
127.118419 1
1.0%
127.097033 1
1.0%
127.084807 1
1.0%
127.061674 1
1.0%
127.056132 1
1.0%
127.051229 1
1.0%
127.048949 1
1.0%
127.048548 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct88
Distinct (%)90.7%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean37.555883
Minimum37.462079
Maximum37.642978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:53.343518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.462079
5-th percentile37.485135
Q137.534593
median37.570299
Q337.57354
95-th percentile37.612926
Maximum37.642978
Range0.180899
Interquartile range (IQR)0.038947

Descriptive statistics

Standard deviation0.036056948
Coefficient of variation (CV)0.00096008788
Kurtosis0.22204773
Mean37.555883
Median Absolute Deviation (MAD)0.010247
Skewness-0.61339979
Sum3642.9207
Variance0.0013001035
MonotonicityNot monotonic
2023-12-10T19:12:53.597008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.563763 2
 
2.0%
37.569949 2
 
2.0%
37.560052 2
 
2.0%
37.560814 2
 
2.0%
37.570299 2
 
2.0%
37.573175 2
 
2.0%
37.574419 2
 
2.0%
37.570685 2
 
2.0%
37.571655 2
 
2.0%
37.574105 1
 
1.0%
Other values (78) 78
78.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
37.462079 1
1.0%
37.477766 1
1.0%
37.477882 1
1.0%
37.479121 1
1.0%
37.48254 1
1.0%
37.485784 1
1.0%
37.4862 1
1.0%
37.488295 1
1.0%
37.494257 1
1.0%
37.499807 1
1.0%
ValueCountFrequency (%)
37.642978 1
1.0%
37.626324 1
1.0%
37.622781 1
1.0%
37.617077 1
1.0%
37.615409 1
1.0%
37.612305 1
1.0%
37.595821 1
1.0%
37.593584 1
1.0%
37.587334 1
1.0%
37.58487 1
1.0%

area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
97 
<NA>
 
3

Length

Max length4
Median length2
Mean length2.06
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T19:12:53.845144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:54.012216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 97
97.0%
na 3
 
3.0%

base_ymd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-12-31
97 
<NA>
 
3

Length

Max length10
Median length10
Mean length9.82
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row<NA>
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T19:12:54.192883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:54.388458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 97
97.0%
na 3
 
3.0%

Interactions

2023-12-10T19:12:48.167144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:46.956708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:47.647120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:48.346002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:47.208173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:47.810235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:48.531292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:47.442619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:47.964959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:12:54.504954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_gn_gu_cdxpos_loypos_la
entrp_nm1.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.000
city_gn_gu_cd1.0001.0001.0000.9430.909
xpos_lo1.0001.0000.9431.0000.900
ypos_la1.0001.0000.9090.9001.000
2023-12-10T19:12:54.659581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmcity_do_cdbase_ymd
area_nm1.0001.0001.000
city_do_cd1.0001.0001.000
base_ymd1.0001.0001.000
2023-12-10T19:12:54.820867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_cdxpos_loypos_lacity_do_cdarea_nmbase_ymd
city_gn_gu_cd1.0000.113-0.6501.0001.0001.000
xpos_lo0.1131.000-0.1881.0001.0001.000
ypos_la-0.650-0.1881.0001.0001.0001.000
city_do_cd1.0001.0001.0001.0001.0001.000
area_nm1.0001.0001.0001.0001.0001.000
base_ymd1.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T19:12:48.800264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:12:49.057021image/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.
2023-12-10T19:12:49.364568image/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

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmbase_ymd
0라일라라일라서울특별시 강남구 개포로 241 선모빌딩 5층1111680127.04894937.479121서울2020-12-31
1천봉나전칠기체험관경기도 양주시 장흥면 석현리 394<NA><NA><NA><NA><NA><NA>
2임희숙침선방서울특별시 강남구 개포로22길 191111680127.04854837.477766서울2020-12-31
3백학조선민화연구소서울특별시 강남구 도산대로11길 46 1층1111680127.02246237.520488서울2020-12-31
4마인드플랫폼서울특별시 강남구 도산대로81길 5 세영빌딩 3층1111680127.04781537.524822서울2020-12-31
5havebeenseoul서울특별시 강남구 봉은사로18길 17 1층1111680127.03046837.505002서울2020-12-31
6고려아트샵서울특별시 강남구 선릉로 704 12F 1258-5호(청담벤처프라자)1111680127.04139937.517814서울2020-12-31
7예술가경기도 파주시 광탄면 부흥로275번길 22-2복사<NA><NA><NA><NA><NA><NA>
8스타기프트서울특별시 강남구 테헤란로 152 강남파이낸스센터1111680127.03650437.500032서울2020-12-31
9씽즈코리언서울특별시 강남구 테헤란로 3131111680127.04525537.503789서울2020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmbase_ymd
90공예가서울특별시 종로구 청계천로 2111111110127.00104537.569794서울2020-12-31
91우신공예서울특별시 중구 난계로11길 91111140127.02290137.565751서울2020-12-31
92색동칠보서울특별시 중구 남대문로 141111140126.97721537.560814서울2020-12-31
93더윤칠보서울특별시 중구 남대문로 14 남대문로지하쇼핑센터 남대문지하도상가 27호1111140126.97721537.560814서울2020-12-31
94신세기공예사서울특별시 중구 남대문시장4길 31111140126.97772437.560052서울2020-12-31
95태극옻칠공예서울특별시 중구 남대문시장4길 3 중앙상가 3층 23 24호1111140126.97772437.560052서울2020-12-31
96제일토산품서울특별시 중구 남대문시장길 291111140126.97761737.560385서울2020-12-31
97샵오브코리아서울특별시 중구 서소문로 221111140126.96449937.559532서울2020-12-31
98꼬레아 소공지하쇼핑센터점서울특별시 중구 소공로 102 소공지하쇼핑센터 31-11111140126.97966537.563763서울2020-12-31
99샘물 소공지하쇼핑센터점서울특별시 중구 소공로 102 소공지하쇼핑센터 641111140126.97966537.563763서울2020-12-31