Overview

Dataset statistics

Number of variables8
Number of observations122
Missing cells34
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory66.1 B

Variable types

Numeric1
DateTime1
Categorical2
Text4

Dataset

Description서울특별시 양천구에 위치한 행정사 사무소 주소, 신고연월일, 대표자, 명칭, 전화번호 영업상태 등의 정보를 제공합니다.
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15039328/fileData.do

Alerts

영업상태 has constant value ""Constant
행정사 종류 is highly imbalanced (83.4%)Imbalance
전화번호 has 34 (27.9%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:46:48.334347
Analysis finished2023-12-12 00:46:49.537908
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.5
Minimum1
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T09:46:49.665192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.05
Q131.25
median61.5
Q391.75
95-th percentile115.95
Maximum122
Range121
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation35.362409
Coefficient of variation (CV)0.57499853
Kurtosis-1.2
Mean61.5
Median Absolute Deviation (MAD)30.5
Skewness0
Sum7503
Variance1250.5
MonotonicityStrictly increasing
2023-12-12T09:46:49.846184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
93 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
Other values (112) 112
91.8%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
Distinct100
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum1996-02-21 00:00:00
Maximum2023-06-19 00:00:00
2023-12-12T09:46:50.000402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:50.190892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

행정사 종류
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
일반행정사
119 
외국어번역행정사(영어)
 
3

Length

Max length12
Median length5
Mean length5.1721311
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반행정사
2nd row일반행정사
3rd row일반행정사
4th row일반행정사
5th row일반행정사

Common Values

ValueCountFrequency (%)
일반행정사 119
97.5%
외국어번역행정사(영어) 3
 
2.5%

Length

2023-12-12T09:46:50.369982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:46:50.509615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반행정사 119
97.5%
외국어번역행정사(영어 3
 
2.5%

성명
Text

Distinct118
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:46:50.881190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters366
Distinct characters103
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

Unique114 ?
Unique (%)93.4%

Sample

1st row남창국
2nd row이준기
3rd row이동연
4th row이장영
5th row김형남
ValueCountFrequency (%)
계병억 2
 
1.6%
김연옥 2
 
1.6%
김영호 2
 
1.6%
이백택 2
 
1.6%
정연준 1
 
0.8%
최병철 1
 
0.8%
조병찬 1
 
0.8%
장선식 1
 
0.8%
이삼철 1
 
0.8%
안대환 1
 
0.8%
Other values (108) 108
88.5%
2023-12-12T09:46:51.438203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
8.5%
23
 
6.3%
16
 
4.4%
13
 
3.6%
12
 
3.3%
9
 
2.5%
9
 
2.5%
8
 
2.2%
7
 
1.9%
7
 
1.9%
Other values (93) 231
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
31
 
8.5%
23
 
6.3%
16
 
4.4%
13
 
3.6%
12
 
3.3%
9
 
2.5%
9
 
2.5%
8
 
2.2%
7
 
1.9%
7
 
1.9%
Other values (93) 231
63.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
31
 
8.5%
23
 
6.3%
16
 
4.4%
13
 
3.6%
12
 
3.3%
9
 
2.5%
9
 
2.5%
8
 
2.2%
7
 
1.9%
7
 
1.9%
Other values (93) 231
63.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
31
 
8.5%
23
 
6.3%
16
 
4.4%
13
 
3.6%
12
 
3.3%
9
 
2.5%
9
 
2.5%
8
 
2.2%
7
 
1.9%
7
 
1.9%
Other values (93) 231
63.1%

명칭
Text

Distinct117
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:46:51.836527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.1557377
Min length3

Characters and Unicode

Total characters995
Distinct characters163
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

Unique112 ?
Unique (%)91.8%

Sample

1st row자봉 행정사 사무소
2nd row해냄 행정사 사무소
3rd row연 행정사사무소
4th row서원행정사합동사무소
5th row푸른코리아 행정사사무소
ValueCountFrequency (%)
행정사사무소 14
 
9.2%
사무소 6
 
3.9%
행정사 6
 
3.9%
계병억 2
 
1.3%
세움k행정사사무소 2
 
1.3%
김영호 2
 
1.3%
이백택 2
 
1.3%
길림행정사 2
 
1.3%
법무사 2
 
1.3%
최백기행정사사무실 1
 
0.7%
Other values (113) 113
74.3%
2023-12-12T09:46:52.334428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
19.3%
114
 
11.5%
107
 
10.8%
96
 
9.6%
85
 
8.5%
30
 
3.0%
15
 
1.5%
13
 
1.3%
9
 
0.9%
7
 
0.7%
Other values (153) 327
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 943
94.8%
Space Separator 30
 
3.0%
Uppercase Letter 8
 
0.8%
Other Punctuation 4
 
0.4%
Lowercase Letter 4
 
0.4%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
192
20.4%
114
 
12.1%
107
 
11.3%
96
 
10.2%
85
 
9.0%
15
 
1.6%
13
 
1.4%
9
 
1.0%
7
 
0.7%
7
 
0.7%
Other values (138) 298
31.6%
Uppercase Letter
ValueCountFrequency (%)
K 3
37.5%
C 3
37.5%
H 1
 
12.5%
D 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
t 1
25.0%
i 1
25.0%
w 1
25.0%
h 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
& 2
50.0%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 943
94.8%
Common 40
 
4.0%
Latin 12
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
192
20.4%
114
 
12.1%
107
 
11.3%
96
 
10.2%
85
 
9.0%
15
 
1.6%
13
 
1.4%
9
 
1.0%
7
 
0.7%
7
 
0.7%
Other values (138) 298
31.6%
Latin
ValueCountFrequency (%)
K 3
25.0%
C 3
25.0%
H 1
 
8.3%
t 1
 
8.3%
i 1
 
8.3%
w 1
 
8.3%
h 1
 
8.3%
D 1
 
8.3%
Common
ValueCountFrequency (%)
30
75.0%
. 2
 
5.0%
) 2
 
5.0%
& 2
 
5.0%
( 2
 
5.0%
2 1
 
2.5%
4 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 943
94.8%
ASCII 52
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
192
20.4%
114
 
12.1%
107
 
11.3%
96
 
10.2%
85
 
9.0%
15
 
1.6%
13
 
1.4%
9
 
1.0%
7
 
0.7%
7
 
0.7%
Other values (138) 298
31.6%
ASCII
ValueCountFrequency (%)
30
57.7%
K 3
 
5.8%
C 3
 
5.8%
. 2
 
3.8%
) 2
 
3.8%
& 2
 
3.8%
( 2
 
3.8%
2 1
 
1.9%
4 1
 
1.9%
H 1
 
1.9%
Other values (5) 5
 
9.6%
Distinct105
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:46:52.761642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length47
Mean length31.393443
Min length21

Characters and Unicode

Total characters3830
Distinct characters140
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)76.2%

Sample

1st row서울특별시 양천구 목동동로 100, 1301동 602호 (신정동, 목동신시가지아파트13단지)
2nd row서울특별시 양천구 남부순환로83길 47, 상가동 2층 제4-30호 (신월동, 목동 센트럴 아이파크 위브 4단지)
3rd row서울특별시 양천구 목동동로 350, 510동 503호 (목동, 목동신시가지아파트5단지)
4th row서울특별시 양천구 신월로 389, 510호,511호(신정동)
5th row서울특별시 양천구 목동동로 293, 현대41타워 3601호(목동)
ValueCountFrequency (%)
서울특별시 122
 
16.5%
양천구 122
 
16.5%
신정동 54
 
7.3%
신정동, 23
 
3.1%
목동 22
 
3.0%
신월로 20
 
2.7%
목동동로10길 17
 
2.3%
목동동로 12
 
1.6%
목동서로 10
 
1.4%
목동, 7
 
0.9%
Other values (232) 331
44.7%
2023-12-12T09:46:53.708082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
618
 
16.1%
250
 
6.5%
1 153
 
4.0%
135
 
3.5%
132
 
3.4%
129
 
3.4%
0 126
 
3.3%
124
 
3.2%
123
 
3.2%
122
 
3.2%
Other values (130) 1918
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2208
57.7%
Decimal Number 642
 
16.8%
Space Separator 618
 
16.1%
Close Punctuation 119
 
3.1%
Open Punctuation 119
 
3.1%
Other Punctuation 97
 
2.5%
Dash Punctuation 15
 
0.4%
Uppercase Letter 12
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
250
 
11.3%
135
 
6.1%
132
 
6.0%
129
 
5.8%
124
 
5.6%
123
 
5.6%
122
 
5.5%
122
 
5.5%
122
 
5.5%
122
 
5.5%
Other values (105) 827
37.5%
Decimal Number
ValueCountFrequency (%)
1 153
23.8%
0 126
19.6%
3 74
11.5%
2 74
11.5%
4 49
 
7.6%
5 42
 
6.5%
9 37
 
5.8%
8 36
 
5.6%
7 29
 
4.5%
6 22
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
M 3
25.0%
S 2
16.7%
E 1
 
8.3%
B 1
 
8.3%
U 1
 
8.3%
C 1
 
8.3%
O 1
 
8.3%
Y 1
 
8.3%
A 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
96
99.0%
& 1
 
1.0%
Space Separator
ValueCountFrequency (%)
618
100.0%
Close Punctuation
ValueCountFrequency (%)
) 119
100.0%
Open Punctuation
ValueCountFrequency (%)
( 119
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2208
57.7%
Common 1610
42.0%
Latin 12
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
250
 
11.3%
135
 
6.1%
132
 
6.0%
129
 
5.8%
124
 
5.6%
123
 
5.6%
122
 
5.5%
122
 
5.5%
122
 
5.5%
122
 
5.5%
Other values (105) 827
37.5%
Common
ValueCountFrequency (%)
618
38.4%
1 153
 
9.5%
0 126
 
7.8%
) 119
 
7.4%
( 119
 
7.4%
96
 
6.0%
3 74
 
4.6%
2 74
 
4.6%
4 49
 
3.0%
5 42
 
2.6%
Other values (6) 140
 
8.7%
Latin
ValueCountFrequency (%)
M 3
25.0%
S 2
16.7%
E 1
 
8.3%
B 1
 
8.3%
U 1
 
8.3%
C 1
 
8.3%
O 1
 
8.3%
Y 1
 
8.3%
A 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2208
57.7%
ASCII 1526
39.8%
None 96
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
618
40.5%
1 153
 
10.0%
0 126
 
8.3%
) 119
 
7.8%
( 119
 
7.8%
3 74
 
4.8%
2 74
 
4.8%
4 49
 
3.2%
5 42
 
2.8%
9 37
 
2.4%
Other values (14) 115
 
7.5%
Hangul
ValueCountFrequency (%)
250
 
11.3%
135
 
6.1%
132
 
6.0%
129
 
5.8%
124
 
5.6%
123
 
5.6%
122
 
5.5%
122
 
5.5%
122
 
5.5%
122
 
5.5%
Other values (105) 827
37.5%
None
ValueCountFrequency (%)
96
100.0%

전화번호
Text

MISSING 

Distinct80
Distinct (%)90.9%
Missing34
Missing (%)27.9%
Memory size1.1 KiB
2023-12-12T09:46:54.040831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.806818
Min length11

Characters and Unicode

Total characters1039
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

Unique72 ?
Unique (%)81.8%

Sample

1st row02-595-8006
2nd row02-2069-0701
3rd row02-2647-6500
4th row02-2691-4700
5th row070-8688-6551
ValueCountFrequency (%)
02-651-0199 2
 
2.3%
02-598-8334 2
 
2.3%
02-2654-8844 2
 
2.3%
02-2645-9968 2
 
2.3%
02-649-4030 2
 
2.3%
02-6369-4976 2
 
2.3%
02-2643-5050 2
 
2.3%
02-647-8630 2
 
2.3%
02-848-8388 1
 
1.1%
02-2608-2200 1
 
1.1%
Other values (70) 70
79.5%
2023-12-12T09:46:54.698109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 194
18.7%
0 177
17.0%
- 176
16.9%
6 120
11.5%
4 64
 
6.2%
8 64
 
6.2%
5 57
 
5.5%
9 55
 
5.3%
3 51
 
4.9%
7 42
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 863
83.1%
Dash Punctuation 176
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 194
22.5%
0 177
20.5%
6 120
13.9%
4 64
 
7.4%
8 64
 
7.4%
5 57
 
6.6%
9 55
 
6.4%
3 51
 
5.9%
7 42
 
4.9%
1 39
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1039
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 194
18.7%
0 177
17.0%
- 176
16.9%
6 120
11.5%
4 64
 
6.2%
8 64
 
6.2%
5 57
 
5.5%
9 55
 
5.3%
3 51
 
4.9%
7 42
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 194
18.7%
0 177
17.0%
- 176
16.9%
6 120
11.5%
4 64
 
6.2%
8 64
 
6.2%
5 57
 
5.5%
9 55
 
5.3%
3 51
 
4.9%
7 42
 
4.0%

영업상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
영업중
122 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업중
2nd row영업중
3rd row영업중
4th row영업중
5th row영업중

Common Values

ValueCountFrequency (%)
영업중 122
100.0%

Length

2023-12-12T09:46:54.882508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:46:55.012609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 122
100.0%

Interactions

2023-12-12T09:46:49.128496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:46:55.102361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호신고연월일행정사 종류전화번호
일련번호1.0000.9220.2820.991
신고연월일0.9221.0000.3830.997
행정사 종류0.2820.3831.0000.000
전화번호0.9910.9970.0001.000
2023-12-12T09:46:55.242718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호행정사 종류
일련번호1.0000.208
행정사 종류0.2081.000

Missing values

2023-12-12T09:46:49.293169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:46:49.464060image/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

일련번호신고연월일행정사 종류성명명칭소재지전화번호영업상태
012023-06-15일반행정사남창국자봉 행정사 사무소서울특별시 양천구 목동동로 100, 1301동 602호 (신정동, 목동신시가지아파트13단지)<NA>영업중
122023-06-15일반행정사이준기해냄 행정사 사무소서울특별시 양천구 남부순환로83길 47, 상가동 2층 제4-30호 (신월동, 목동 센트럴 아이파크 위브 4단지)<NA>영업중
232023-04-04일반행정사이동연연 행정사사무소서울특별시 양천구 목동동로 350, 510동 503호 (목동, 목동신시가지아파트5단지)<NA>영업중
342023-02-14일반행정사이장영서원행정사합동사무소서울특별시 양천구 신월로 389, 510호,511호(신정동)02-595-8006영업중
452022-10-26일반행정사김형남푸른코리아 행정사사무소서울특별시 양천구 목동동로 293, 현대41타워 3601호(목동)<NA>영업중
562022-09-26일반행정사정남용행복한세상 행정사사무소서울특별시 양천구 신목로 95-1, 1층(목동)<NA>영업중
672023-06-19일반행정사이태세이태세행정사사무소서울특별시 양천구 신목로5길 9, 306호 (신정동, 대림아파트)<NA>영업중
782022-01-25일반행정사이주현이안 행정사사무소서울특별시 양천구 목동서로 400, 1034동 1507호(신정동, 목동신시가지아파트10단지)02-2069-0701영업중
892020-06-30일반행정사이창휴좋은집 행정사사무소서울특별시 양천구 목동중앙서로 20 (목동)02-2647-6500영업중
9102022-01-14일반행정사신상원국민D&C 행정사사무소서울특별시 양천구 목동중앙서로7나길 12-30, 목동팰리스 401호(목동)<NA>영업중
일련번호신고연월일행정사 종류성명명칭소재지전화번호영업상태
1121132001-08-28일반행정사이병호행정사이병호사무소서울특별시 양천구 오목로42길 2 (신정동)02-696-0862영업중
1131142000-01-27일반행정사김만호제일행정사서울특별시 양천구 목동동로 100 (신정동)02-2648-1001영업중
1141151999-02-01일반행정사유만영유만영서울특별시 양천구 목동중앙북로2길 8 (목동)02-644-0981영업중
1151162004-06-10일반행정사김문석행정사김문석사무소서울특별시 양천구 곰달래로13길 46-1 (신월동)02-2605-9003영업중
1161171997-01-27일반행정사이백택이백택서울특별시 양천구 목동로 212, 711동 1003호 (목동, 목동아파트)02-647-8630영업중
1171181996-11-04일반행정사김영호김영호서울특별시 양천구 목동서로 400, 1019동 102호 (신정동, 목동아파트)02-651-0199영업중
1181191996-02-21일반행정사계병억계병억서울특별시 양천구 목동중앙본로 118 (목동)02-649-4030영업중
1191201997-01-27일반행정사이백택이백택서울특별시 양천구 목동로 212, 711동 1003호 (목동, 목동아파트)02-647-8630영업중
1201211996-11-04일반행정사김영호김영호서울특별시 양천구 목동서로 400, 1019동 102호 (신정동, 목동아파트)02-651-0199영업중
1211221996-02-21일반행정사계병억계병억서울특별시 양천구 목동중앙본로 118 (목동)02-649-4030영업중